diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml index df11b6257..b25f0ed8e 100644 --- a/.github/workflows/docker-build.yml +++ b/.github/workflows/docker-build.yml @@ -79,7 +79,7 @@ jobs: dockerfile: src/envs/atari_env/server/Dockerfile - name: git-env dockerfile: src/envs/git_env/server/Dockerfile - - name: my-env # Add your environment here + - name: connect4-env # Add your environment here dockerfile: src/envs/connect4_env/server/Dockerfile - name: textarena-env dockerfile: src/envs/textarena_env/server/Dockerfile @@ -87,6 +87,8 @@ jobs: dockerfile: src/envs/browsergym_env/server/Dockerfile - name: snake-env dockerfile: src/envs/snake_env/server/Dockerfile + - name: doom-env + dockerfile: src/envs/doom_env/server/Dockerfile steps: - name: Checkout code diff --git a/.gitignore b/.gitignore index fed309b07..6f1712282 100644 --- a/.gitignore +++ b/.gitignore @@ -108,4 +108,11 @@ outputs/ /uv.lock .uv/ +# ViZDoom generated files +**/_vizdoom.ini +_vizdoom.ini + *.backup*/ + +# .sesskey +*.sesskey diff --git a/docs/environments.md b/docs/environments.md index ab9b3624f..da8a2367e 100644 --- a/docs/environments.md +++ b/docs/environments.md @@ -200,6 +200,25 @@ The OpenEnv community has built a catalog of ready-to-run environments that cove +
+
+ Doom +

+ ViZDoom-powered 3D environment for visual RL research with multiple scenarios, configurable resolution, and real-time rendering. +

+
+
+ + + + + + +
+
+ > Want to publish your own environment? Head over to the [Build Your Own Environment](environment-builder.md) guide for a step-by-step walkthrough. diff --git a/docs/environments/doom.md b/docs/environments/doom.md new file mode 100644 index 000000000..b5b9e0256 --- /dev/null +++ b/docs/environments/doom.md @@ -0,0 +1 @@ +--8<-- "../../src/envs/doom_env/README.md" diff --git a/examples/doom_example.py b/examples/doom_example.py new file mode 100755 index 000000000..95e34e954 --- /dev/null +++ b/examples/doom_example.py @@ -0,0 +1,245 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Example usage of the Doom Environment. + +This script demonstrates how to use the Doom environment with OpenEnv. +It can be run in two modes: +1. With Docker: Uses the Docker image to run the environment +2. Local: Directly uses the DoomEnvironment class (requires ViZDoom installed) + +Both modes support rendering if the appropriate libraries are installed. +""" + +import argparse +import sys +import time +from pathlib import Path + +import numpy as np + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + + +def run_with_docker(render: bool = False, num_steps: int = 100): + """Run Doom environment using Docker container.""" + from envs.doom_env import DoomAction, DoomEnv + + print("Starting Doom environment with Docker...") + try: + # Create environment from Docker image + env = DoomEnv.from_docker_image("doom-env:latest") + + # Reset to start a new episode + result = env.reset() + print(f"✓ Environment reset") + print(f" Screen shape: {result.observation.screen_shape}") + print(f" Available actions: {result.observation.available_actions}") + print(f" Game variables: {result.observation.game_variables}") + + if render: + print(f"\n✓ Rendering enabled") + print(f" Note: Rendering uses the observation from the server") + print( + f" For best performance, consider using local mode with window_visible=True" + ) + print(f" Alternative: Use the web interface at http://localhost:8000/web") + print( + f" Make sure opencv-python or matplotlib is installed locally for rendering" + ) + + # Run for a few steps + print(f"\nRunning episode for {num_steps} steps...") + for i in range(num_steps): + # Take a random action from available actions + available_actions = result.observation.available_actions + if available_actions: + action_id = int( + np.random.choice(available_actions) + ) # Convert to Python int + else: + action_id = 0 + + result = env.step(DoomAction(action_id=action_id)) + + # Render if requested + if render: + env.render() + + if i % 10 == 0 or result.observation.done: + print(f"Step {i+1}:") + print(f" Action: {action_id}") + print(f" Reward: {result.reward}") + print(f" Done: {result.observation.done}") + if result.observation.game_variables: + print(f" Game vars: {result.observation.game_variables}") + + if result.observation.done: + print(f"\n✓ Episode finished at step {i+1}!") + break + + # Small delay for rendering to be visible + if render: + time.sleep(0.03) + + finally: + print("\nCleaning up...") + env.close() + print("✓ Environment closed") + + +def run_local(render: bool = False, num_steps: int = 100): + """Run Doom environment locally without Docker.""" + try: + from envs.doom_env.models import DoomAction + from envs.doom_env.server.doom_env_environment import DoomEnvironment + except ImportError as e: + print(f"Error: Could not import environment components: {e}") + print("Make sure ViZDoom is installed: pip install vizdoom") + return + + print("Starting Doom environment locally...") + try: + # Create environment + # Note: When using local mode, you can enable window_visible=True + # for native ViZDoom rendering (most efficient) + env = DoomEnvironment( + scenario="basic", + screen_resolution="RES_320X240", # Higher resolution for better visibility + screen_format="RGB24", + window_visible=render, # Use native ViZDoom window if rendering + use_discrete_actions=True, + ) + + # Reset to start a new episode + obs = env.reset() + print(f"✓ Environment reset") + print(f" Screen shape: {obs.screen_shape}") + print(f" Available actions: {obs.available_actions}") + print(f" Game variables: {obs.game_variables}") + + if render: + print(f"\n✓ Rendering enabled") + if env.window_visible: + print(f" Using native ViZDoom window (most efficient)") + else: + print(f" Using Python rendering (cv2/matplotlib)") + + # Run for a few steps + print(f"\nRunning episode for {num_steps} steps...") + for i in range(num_steps): + # Take a random action + if obs.available_actions: + action_id = int( + np.random.choice(obs.available_actions) + ) # Convert to Python int + else: + action_id = 0 + + obs = env.step(DoomAction(action_id=action_id)) + + # Render if requested and not using native window + if render and not env.window_visible: + env.render() + + if i % 10 == 0 or obs.done: + print(f"Step {i+1}:") + print(f" Action: {action_id}") + print(f" Reward: {obs.reward}") + print(f" Done: {obs.done}") + if obs.game_variables: + print(f" Game vars: {obs.game_variables}") + + if obs.done: + print(f"\n✓ Episode finished at step {i+1}!") + break + + # Small delay for rendering to be visible + if render: + time.sleep(0.03) + + # Visualize final frame statistics + if not obs.done: + screen = np.array(obs.screen_buffer).reshape(obs.screen_shape) + print(f"\nFinal screen statistics:") + print(f" Shape: {screen.shape}") + print(f" Dtype: {screen.dtype}") + print(f" Min: {screen.min()}, Max: {screen.max()}") + + except Exception as e: + print(f"Error running environment: {e}") + import traceback + + traceback.print_exc() + finally: + if "env" in locals(): + env.close() + print("✓ Environment closed") + + +def main(): + """Main entry point.""" + parser = argparse.ArgumentParser( + description="Run Doom environment example", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Run with Docker (default) + python example.py + + # Run with Docker and rendering + python example.py --render + + # Run locally without Docker + python example.py --local + + # Run locally with rendering (uses native ViZDoom window) + python example.py --local --render + + # Run for more steps + python example.py --local --render --steps 300 + +Note: Rendering requires opencv-python or matplotlib: + pip install opencv-python + # or + pip install matplotlib + """, + ) + parser.add_argument( + "--docker", action="store_true", default=False, help="Run with Docker container" + ) + parser.add_argument( + "--local", action="store_true", help="Run locally without Docker" + ) + parser.add_argument( + "--render", + action="store_true", + help="Enable rendering (shows game window or visualization)", + ) + parser.add_argument( + "--steps", + type=int, + default=100, + help="Number of steps to run (default: 100)", + ) + + args = parser.parse_args() + + # Default to docker if neither specified + if not args.local and not args.docker: + args.docker = True + + if args.local: + run_local(render=args.render, num_steps=args.steps) + else: + run_with_docker(render=args.render, num_steps=args.steps) + + +if __name__ == "__main__": + main() diff --git a/examples/doom_visualizer.py b/examples/doom_visualizer.py new file mode 100644 index 000000000..70356a364 --- /dev/null +++ b/examples/doom_visualizer.py @@ -0,0 +1,438 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Doom Environment Visualizer. + +This script connects to a running Doom environment server and provides +real-time visualization of the game. Use this to see what the agent sees +when the container is running. + +Prerequisites: + 1. Build the Docker image: + docker build -t doom-env:latest -f src/envs/doom_env/server/Dockerfile . + + 2. Install visualization library locally: + pip install opencv-python # Recommended (supports keyboard controls) + # or + pip install matplotlib # Fallback (no keyboard controls) + +Usage: + # Terminal 1: Start the Docker container + docker run -p 8000:8000 doom-env:latest + + # Terminal 2: Run this visualizer + cd examples + python doom_visualizer.py + + # With higher resolution (recommended for better visibility) + docker run -p 8000:8000 -e DOOM_SCREEN_RESOLUTION=RES_640X480 doom-env:latest + python doom_visualizer.py + + # Check what resolution the server is using + python doom_visualizer.py --resolution-info + + # Connect to a different server + python doom_visualizer.py --url http://localhost:8000 + + # Use matplotlib instead of OpenCV + python doom_visualizer.py --matplotlib + +Controls (OpenCV mode): + - Arrow keys or A/D: Move left/right + - Space: Shoot + - Q or ESC: Quit + +Notes: + - The visualizer displays the game in real-time with health/reward info + - Docker resolution determines image quality (change with -e DOOM_SCREEN_RESOLUTION) + - Window automatically scales based on game resolution + - For best quality: Use RES_640X480 or RES_800X600 on Docker side +""" + +import argparse +import sys +import time +from pathlib import Path + +import numpy as np + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +try: + import cv2 + + HAS_CV2 = True +except ImportError: + HAS_CV2 = False + try: + import matplotlib.pyplot as plt + + HAS_MPL = True + except ImportError: + HAS_MPL = False + + +def visualize_with_cv2(env): + """ + Visualize using OpenCV with keyboard controls. + + This function creates an interactive window where you can control the Doom agent + using keyboard inputs. The window displays the game screen with overlaid info + (reward, health, episode status). + + Controls: + - A or Left Arrow: Move left (action_id=1) + - D or Right Arrow: Move right (action_id=2) + - Space: Shoot/Attack (action_id=3) + - Q or ESC: Quit visualizer + + Args: + env: Connected DoomEnv client instance + + Notes: + - Window size automatically scales based on game resolution + - For 160x120 game: Window is ~1024x768 (6x scale) + - For 640x480 game: Window is ~1024x768 (1.6x scale) + - Uses INTER_NEAREST interpolation to preserve pixel art aesthetic + """ + from doom_env import DoomAction + + print("\nControls:") + print(" Arrow Left / A: Move left") + print(" Arrow Right / D: Move right") + print(" Space: Shoot") + print(" Q or ESC: Quit") + print("\nStarting visualization...") + + result = env.reset() + window_name = "Doom Environment - Press Q to quit" + + # Get game resolution + height, width = result.observation.screen_shape[:2] + print(f"Game resolution: {width}x{height}") + + # Calculate window size - make it much larger for better visibility + # Target at least 800 pixels wide + target_width = 1024 + scale_factor = max(1, target_width // width) + window_width = width * scale_factor + window_height = height * scale_factor + + print(f"Window size: {window_width}x{window_height} (scaled {scale_factor}x)") + + # Create window and set size + cv2.namedWindow(window_name, cv2.WINDOW_NORMAL) + cv2.resizeWindow(window_name, window_width, window_height) + + # Action mapping for keyboard + # For basic scenario: [0=noop, 1=left, 2=right, 3=attack] + running = True + while running: + # Get screen + screen = np.array(result.observation.screen_buffer, dtype=np.uint8).reshape( + result.observation.screen_shape + ) + + # Convert RGB to BGR for OpenCV + if len(screen.shape) == 3 and screen.shape[2] == 3: + screen_bgr = cv2.cvtColor(screen, cv2.COLOR_RGB2BGR) + else: + screen_bgr = screen + + # Resize the image for better display (use interpolation) + screen_bgr = cv2.resize( + screen_bgr, + (window_width, window_height), + interpolation=cv2.INTER_NEAREST, # Use INTER_NEAREST to preserve pixel art look + ) + + # Add info overlay + info_text = [ + f"Reward: {result.reward:.1f}", + ( + f"Health: {result.observation.game_variables[0]:.0f}" + if result.observation.game_variables + else "Health: N/A" + ), + f"Done: {result.observation.done}", + f"Actions: {result.observation.available_actions}", + ] + y_pos = 30 + for text in info_text: + cv2.putText( + screen_bgr, + text, + (10, y_pos), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + (0, 255, 0), + 1, + cv2.LINE_AA, + ) + y_pos += 25 + + cv2.imshow(window_name, screen_bgr) + + # Handle keyboard input + key = cv2.waitKey(30) & 0xFF + action_id = 0 # Default: no action + + if key == ord("q") or key == 27: # Q or ESC + break + elif key == ord("a") or key == 81: # A or Left arrow + action_id = 1 # Move left + elif key == ord("d") or key == 83: # D or Right arrow + action_id = 2 # Move right + elif key == ord(" "): # Space + action_id = 3 # Attack + else: + pass + # Random action for demonstration + # if result.observation.available_actions: + # action_id = int(np.random.choice(result.observation.available_actions)) + + # Take action + result = env.step(DoomAction(action_id=action_id)) + + if result.observation.done: + print("\nEpisode finished! Resetting...") + result = env.reset() + time.sleep(1) + + cv2.destroyAllWindows() + + +def visualize_with_matplotlib(env): + """ + Visualize using Matplotlib (automatic random actions). + + This function creates a matplotlib figure that displays the game in real-time. + The agent takes random actions automatically. Good for demonstration or if + OpenCV is not available. + + Controls: + - Ctrl+C: Stop visualizer + - Close window: Stop visualizer + + Args: + env: Connected DoomEnv client instance + + Notes: + - No keyboard controls (random actions only) + - Slower than OpenCV + - Updates every 30ms + - Shows step count, reward, health in title + """ + from doom_env import DoomAction + + print("\nStarting visualization (matplotlib mode - no keyboard controls)...") + print("Taking random actions. Close the window to quit.") + + result = env.reset() + plt.ion() + plt.figure(figsize=(10, 7)) + + step = 0 + try: + while True: + plt.clf() + + # Get and display screen + screen = np.array(result.observation.screen_buffer, dtype=np.uint8).reshape( + result.observation.screen_shape + ) + + plt.imshow(screen) + plt.axis("off") + + # Add info + info = f"Step: {step} | Reward: {result.reward:.1f}" + if result.observation.game_variables: + info += f" | Health: {result.observation.game_variables[0]:.0f}" + info += f" | Done: {result.observation.done}" + plt.title(info, fontsize=10, pad=10) + + plt.pause(0.03) + + # Take random action + if result.observation.available_actions: + action_id = int(np.random.choice(result.observation.available_actions)) + else: + action_id = 0 + + result = env.step(DoomAction(action_id=action_id)) + step += 1 + + if result.observation.done: + print(f"\nEpisode finished at step {step}! Resetting...") + result = env.reset() + step = 0 + time.sleep(1) + + except KeyboardInterrupt: + print("\nStopped by user") + finally: + plt.close("all") + + +def main(): + """ + Main entry point for the Doom environment visualizer. + + This function: + 1. Parses command-line arguments + 2. Checks for required visualization libraries (cv2 or matplotlib) + 3. Connects to the Doom environment server + 4. Launches the appropriate visualization mode + 5. Handles cleanup on exit + + Command-line arguments: + --url: Server URL (default: http://localhost:8000) + --matplotlib: Use matplotlib instead of OpenCV + --resolution-info: Show server resolution and exit + + Examples: + # Basic usage (connects to localhost:8000) + python doom_visualizer.py + + # Check server resolution + python doom_visualizer.py --resolution-info + + # Use matplotlib + python doom_visualizer.py --matplotlib + + # Connect to remote server + python doom_visualizer.py --url http://remote-server:8000 + + Requirements: + - Running Doom environment server + - opencv-python OR matplotlib installed locally + """ + parser = argparse.ArgumentParser( + description="Visualize Doom environment in real-time", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Basic usage - connect to local Docker container + python doom_visualizer.py + + # Check what resolution the server is running + python doom_visualizer.py --resolution-info + + # Connect to custom URL + python doom_visualizer.py --url http://localhost:8000 + + # Use matplotlib instead of OpenCV + python doom_visualizer.py --matplotlib + +Complete workflow: + # 1. Rebuild Docker image (if you updated server code) + docker build -t doom-env:latest -f src/envs/doom_env/server/Dockerfile . + + # 2. Start container with higher resolution (recommended) + docker run -p 8000:8000 -e DOOM_SCREEN_RESOLUTION=RES_640X480 doom-env:latest + + # 3. Install visualization library locally + pip install opencv-python + + # 4. Run visualizer + python doom_visualizer.py + +Available resolutions for Docker: + RES_160X120 - 160×120 (default, very small) + RES_320X240 - 320×240 (small) + RES_640X480 - 640×480 (recommended for visualization) + RES_800X600 - 800×600 (large) + RES_1024X768 - 1024×768 (very large) + +Requirements: + pip install opencv-python # Recommended (supports keyboard controls) + # or + pip install matplotlib # Fallback (no keyboard controls) + """, + ) + parser.add_argument( + "--url", + default="http://localhost:8000", + help="URL of the Doom environment server (default: http://localhost:8000)", + ) + parser.add_argument( + "--matplotlib", + action="store_true", + help="Use matplotlib instead of OpenCV for visualization", + ) + parser.add_argument( + "--resolution-info", + action="store_true", + help="Show current server resolution and exit", + ) + + args = parser.parse_args() + + # Check dependencies + if not HAS_CV2 and not HAS_MPL: + print("Error: No visualization library found!") + print("Install one of the following:") + print(" pip install opencv-python (recommended)") + print(" pip install matplotlib") + sys.exit(1) + + # Import and create client + try: + from doom_env import DoomEnv + except ImportError as e: + print(f"Error: Could not import doom_env: {e}") + print("Make sure you're running from the examples/ directory") + sys.exit(1) + + print(f"Connecting to Doom environment at {args.url}...") + try: + env = DoomEnv(base_url=args.url) + print("✓ Connected successfully!") + except Exception as e: + print(f"✗ Failed to connect: {e}") + print("\nMake sure the server is running:") + print(" docker run -p 8000:8000 doom-env:latest") + sys.exit(1) + + # Show resolution info if requested + if args.resolution_info: + result = env.reset() + height, width = result.observation.screen_shape[:2] + print(f"\nServer Information:") + print(f" Screen resolution: {width}x{height}") + print(f" Screen shape: {result.observation.screen_shape}") + print(f"\nTo change resolution, restart Docker with:") + print( + f" docker run -p 8000:8000 -e DOOM_SCREEN_RESOLUTION=RES_640X480 doom-env:latest" + ) + print(f"\nAvailable resolutions:") + print(f" RES_160X120, RES_320X240, RES_640X480, RES_800X600, RES_1024X768") + env.close() + return + + try: + # Choose visualization method + if args.matplotlib or not HAS_CV2: + if not HAS_MPL: + print( + "Error: matplotlib not available. Install with: pip install matplotlib" + ) + sys.exit(1) + visualize_with_matplotlib(env) + else: + visualize_with_cv2(env) + finally: + print("\nClosing environment...") + env.close() + print("✓ Done!") + + +if __name__ == "__main__": + main() diff --git a/src/envs/doom_env/.dockerignore b/src/envs/doom_env/.dockerignore new file mode 100644 index 000000000..fc288e5de --- /dev/null +++ b/src/envs/doom_env/.dockerignore @@ -0,0 +1,15 @@ +.venv +.git +.gitignore +.env +__pycache__/ +*.pyc +*.pyo +*.pyd +*.pyw +*.pyz +*.pywz +*.pyzw +*.pyzwz + + diff --git a/src/envs/doom_env/GIF_GENERATION.md b/src/envs/doom_env/GIF_GENERATION.md new file mode 100644 index 000000000..7a855e0fe --- /dev/null +++ b/src/envs/doom_env/GIF_GENERATION.md @@ -0,0 +1,112 @@ +# Doom Environment - GIF Generation Guide + +This guide explains how to generate GIFs of different Doom scenarios for the README. + +## Quick Start + +```bash +# Navigate to doom_env directory +cd src/envs/doom_env + +# Install with GIF generation dependencies +pip install -e ".[gif_generation]" + +# Or install specific packages +pip install imageio + +# Generate GIFs (this will take a few minutes) +python generate_gifs.py +``` + +## What Gets Generated + +The script will create 4 GIF files in the `assets/` directory: + +1. **basic.gif** - Basic scenario showing simple movement and shooting +2. **deadly_corridor.gif** - Corridor navigation with monsters +3. **defend_the_center.gif** - Defending a central position +4. **health_gathering.gif** - Collecting health packs + +Each GIF will be: +- 80 frames long +- 12 fps (frames per second) +- 320x240 resolution +- Approximately 1-3 MB in size + +## Custom Generation + +You can customize the generation: + +```bash +# Different scenarios +python generate_gifs.py --scenario basic my_way_home take_cover + +# More frames and higher FPS +python generate_gifs.py --frames 150 --fps 20 + +# Higher resolution (larger file size) +python generate_gifs.py --resolution RES_640X480 + +# Single scenario +python generate_gifs.py --scenario basic --frames 100 --fps 15 +``` + +## Available Scenarios + +- `basic` - Simple movement and shooting +- `deadly_corridor` - Navigate corridor with monsters +- `defend_the_center` - Defend central position +- `defend_the_line` - Defend a line against enemies +- `health_gathering` - Collect health packs +- `my_way_home` - Navigate to target location +- `predict_position` - Predict object positions +- `take_cover` - Learn cover mechanics + +## Troubleshooting + +### ImportError: No module named 'imageio' + +Install with GIF generation support: +```bash +pip install -e ".[gif_generation]" +``` + +Or install imageio directly: +```bash +pip install imageio +``` + +### ImportError: No module named 'vizdoom' + +Install the doom_env package (includes ViZDoom): +```bash +pip install -e . +``` + +### Scenario not found + +Make sure you're using one of the built-in scenario names listed above, or provide a full path to a .cfg file: +```bash +python generate_gifs.py --scenario /path/to/custom_scenario.cfg +``` + +## README Integration + +The GIFs are already referenced in the README.md file in the "Scenarios Gallery" section. Once generated, they will automatically display when viewing the README. + +The references look like: +```markdown +![Basic Scenario](assets/basic.gif) +``` + +## Re-generating GIFs + +To update the GIFs with different settings or refresh them: + +1. Delete old GIFs: `rm assets/*.gif` +2. Run the generator with your desired settings +3. The new GIFs will replace the old ones + +## Note + +The script runs headless (no window) and uses random actions to showcase the environments. The GIFs show what the agent sees, not necessarily optimal gameplay. diff --git a/src/envs/doom_env/README.md b/src/envs/doom_env/README.md new file mode 100644 index 000000000..15519e6ed --- /dev/null +++ b/src/envs/doom_env/README.md @@ -0,0 +1,616 @@ +--- +title: Doom Environment Server +emoji: 🎮 +colorFrom: blue +colorTo: green +sdk: docker +pinned: false +app_port: 8000 +base_path: /web +tags: + - openenv + - Doom + - vizDoom + - Reinforcement-Learning +--- + +# Doom Environment + +A ViZDoom-based environment for OpenEnv. ViZDoom is a Doom-based AI research platform for visual reinforcement learning, allowing agents to play Doom using only visual information. Referencing the [ViZDoom documentation](https://vizdoom.cs.put.edu.pl/). And, also for licensing information, please refer to the [ViZDoom license](https://github.com/Farama-Foundation/ViZDoom?tab=readme-ov-file#license) + +## References + +- [ViZDoom Documentation](http://vizdoom.cs.put.edu.pl/) +- [ViZDoom GitHub](https://github.com/mwydmuch/ViZDoom) + +

+ +

+ + +## Overview + +This environment wraps ViZDoom scenarios and exposes them through the OpenEnv API. It provides: +- **Visual observations**: RGB or grayscale screen buffers +- **Game variables**: Health, ammo, kills, etc. +- **Flexible action space**: Discrete actions or button combinations +- **Multiple scenarios**: Built-in scenarios like "basic", "deadly_corridor", "defend_the_center", etc. + +![Basic Scenario](assets/basic.gif) ![Deadly Corridor](assets/deadly_corridor.gif) ![Defend the Center](assets/defend_the_center.gif) ![Health Gathering](assets/health_gathering.gif) ![Line Attach](assets/defend_the_line.gif) ![Predict Position](assets/predict_position.gif) + +## Quick Start + +The simplest way to use the Doom environment is through the `DoomEnv` class: + +```python +from doom_env import DoomAction, DoomEnv + +try: + # Create environment from Docker image + doom_env = DoomEnv.from_docker_image("doom-env:latest") + + # Reset to start a new episode + result = doom_env.reset() + print(f"Screen shape: {result.observation.screen_shape}") + print(f"Available actions: {result.observation.available_actions}") + + # Take actions + for i in range(100): + # Use discrete action (e.g., 0=no-op, 1-7=various actions) + result = doom_env.step(DoomAction(action_id=1)) + + # Optional: Render the game + doom_env.render() # Shows visualization window + + print(f"Step {i}:") + print(f" Reward: {result.reward}") + print(f" Done: {result.observation.done}") + + if result.observation.done: + print("Episode finished!") + break + +finally: + # Always clean up + doom_env.close() +``` + +That's it! The `DoomEnv.from_docker_image()` method handles: +- Starting the Docker container +- Waiting for the server to be ready +- Connecting to the environment +- Container cleanup when you call `close()` + +## Web Interface + +The Doom environment includes a built-in web interface for interactive testing and exploration. The web interface is **enabled by default** in the Docker image. + +```bash +# Start the container with web interface (default) +docker run -p 8000:8000 doom-env:latest + +# Or explicitly enable it +docker run -p 8000:8000 -e ENABLE_WEB_INTERFACE=true doom-env:latest + +# Disable web interface (API only) +docker run -p 8000:8000 -e ENABLE_WEB_INTERFACE=false doom-env:latest + +# Access the web interface in your browser: +# - Interactive UI: http://localhost:8000/web +# - API Documentation: http://localhost:8000/docs +# - Health Check: http://localhost:8000/health +``` + +The web interface provides: +- **Interactive gameplay**: Control the agent through a web UI +- **Real-time visualization**: See what the agent sees +- **API explorer**: Test all endpoints with OpenAPI/Swagger +- **Environment info**: View available actions, scenarios, and configuration + +**Note**: When `ENABLE_WEB_INTERFACE=false`, only the core API endpoints are available (no `/web` interface). + +## Building the Docker Image + +The Doom environment Docker image can be built in standalone mode using only public base images. This makes it suitable for CI/CD, GitHub, and HuggingFace deployments. + +```bash +# Build from project root +docker build -t doom-env:latest -f src/envs/doom_env/server/Dockerfile src/envs/doom_env + +# Or build from the doom_env directory +cd src/envs/doom_env +docker build -t doom-env:latest -f server/Dockerfile . +``` + +**What gets installed:** + +The Dockerfile uses the `pyproject.toml` to install all dependencies: +- **OpenEnv core**: Installed as a dependency +- **Core packages**: FastAPI, Uvicorn, Pydantic, Requests (from pyproject.toml) +- **ViZDoom**: Installed with all system dependencies (SDL2, Boost, OpenGL, etc.) +- **NumPy**: For array operations +- **Web interface support**: Enabled by default via `ENABLE_WEB_INTERFACE=true` + +**Build details:** +- Base image: `python:3.11-slim` (public) +- Installation: Uses `pip install -e` with pyproject.toml +- System deps: ViZDoom build tools and runtime libraries +- Size: ~1.5-2GB (includes ViZDoom system dependencies) + +## Scenarios Gallery + +ViZDoom comes with multiple built-in scenarios for different research tasks: + +### Basic Scenario +Simple environment for learning basic movement and shooting mechanics. + +*Agent learning to navigate and shoot in the basic scenario* + +### Deadly Corridor +Navigate through a corridor while avoiding or eliminating monsters. + +*Agent navigating the deadly corridor* + +### Defend the Center +Stay alive as long as possible while defending the center position. + +*Agent defending the center against waves of enemies* + +### Health Gathering +Collect health packs scattered around the environment to survive. + +*Agent collecting health packs for survival* + +> **Note**: To generate these GIFs yourself, run: +> ```bash +> cd src/envs/doom_env +> pip install numpy imageio vizdoom +> python generate_gifs.py +> ``` + +## Environment Details + +### Action Space + +Actions can be specified in two ways: + +1. **Discrete Actions** (recommended for most use cases): + ```python + DoomAction(action_id=2) # Single integer action + ``` + + Available discrete actions (depends on scenario): + - `0`: No-op (do nothing) + - `1-N`: Various single button presses (move left, right, shoot, etc.) + +2. **Button Combinations**: + ```python + DoomAction(buttons=[1, 0, 1, 0]) # Press specific buttons + ``` + + Each element is 0 (not pressed) or 1 (pressed). + +### Observation Space + +**DoomObservation** contains: +- `screen_buffer` (List[int]): Flattened screen pixels + - RGB: Shape [height, width, 3] before flattening + - Grayscale: Shape [height, width] before flattening +- `screen_shape` (List[int]): Original shape of the screen +- `game_variables` (List[float]): Health, ammo, kills, etc. +- `available_actions` (List[int]): Valid action IDs +- `episode_finished` (bool): Whether episode has ended +- `reward` (float): Reward from last action +- `done` (bool): Same as episode_finished +- `metadata` (dict): Additional info (scenario name, available buttons) + +### Scenarios + +ViZDoom comes with several built-in scenarios: + +- **basic**: Simple scenario to learn basic movement and shooting +- **deadly_corridor**: Navigate a corridor while avoiding/killing monsters +- **defend_the_center**: Stay alive as long as possible in the center +- **defend_the_line**: Defend a line against incoming monsters +- **health_gathering**: Collect health packs to survive +- **my_way_home**: Navigate to a specific location +- **predict_position**: Predict where an object will be +- **take_cover**: Learn to take cover from enemy fire + +## Advanced Usage + +### Custom Configuration + +You can customize the environment when creating the server: + +```python +from doom_env.server.doom_env_environment import DoomEnvironment + +# Create with custom settings +env = DoomEnvironment( + scenario="deadly_corridor", + screen_resolution="RES_320X240", # Higher resolution + screen_format="GRAY8", # Grayscale instead of RGB + window_visible=True, # Show game window + use_discrete_actions=True # Use discrete action space +) +``` + +### Connecting to an Existing Server + +If you already have a Doom environment server running: + +```python +from doom_env import DoomEnv + +# Connect to existing server +doom_env = DoomEnv(base_url="http://localhost:8000") + +# Use as normal +result = doom_env.reset() +result = doom_env.step(DoomAction(action_id=1)) +``` + +Note: When connecting to an existing server, `doom_env.close()` will NOT stop the server. + +### Processing Visual Observations + +The screen buffer is flattened for JSON serialization. To use it: + +```python +import numpy as np + +result = doom_env.reset() +obs = result.observation + +# Reshape to original dimensions +screen = np.array(obs.screen_buffer).reshape(obs.screen_shape) + +# screen is now a numpy array with shape [height, width, channels] +# You can visualize it, pass to a neural network, etc. +``` + +### Rendering + +The Doom environment supports multiple rendering options depending on your use case: + +#### Option 1: Web Interface (Recommended for Docker) + +The easiest way to visualize the game when using Docker: + +```bash +# Start the container with web interface +docker run -p 8000:8000 doom-env:latest + +# Open in your browser: +# http://localhost:8000/web +``` + +**Advantages:** +- No local dependencies needed +- Works in browser +- Interactive controls +- Real-time visualization + +#### Option 2: Client-Side Rendering (Docker Mode) + +Render on your local machine using the screen buffer from Docker: + +```python +from doom_env import DoomAction, DoomEnv + +env = DoomEnv.from_docker_image("doom-env:latest") +result = env.reset() + +for _ in range(100): + result = env.step(DoomAction(action_id=1)) + env.render() # Display using cv2 or matplotlib + +env.close() +``` + +**Requirements:** Install rendering library on your **local machine** (not in Docker): +```bash +pip install opencv-python +# or +pip install matplotlib +``` + +**Note:** This downloads the screen buffer from Docker via HTTP and renders it locally. Works well but has some network overhead. + +#### Option 3: Local Mode with Native Window (Best Performance) + +For the fastest rendering, run locally with ViZDoom's native window: + +```python +from envs.doom_env.server.doom_env_environment import DoomEnvironment +from envs.doom_env.models import DoomAction + +# Native ViZDoom window (most efficient) +env = DoomEnvironment( + scenario="basic", + window_visible=True, # Enable native SDL2 window +) + +obs = env.reset() +for _ in range(100): + obs = env.step(DoomAction(action_id=1)) + # No render() call needed - native window updates automatically + +env.close() +``` + +**Advantages:** +- Native SDL2 rendering (fastest) +- No network overhead +- Smooth real-time gameplay + +#### Rendering Dependencies + +Install optional rendering dependencies: + +```bash +# Using pip +pip install -e ".[rendering]" + +# Or install individually +pip install opencv-python # Preferred for rendering +# or +pip install matplotlib # Fallback option +``` + +#### Render Modes + +Both environments support two render modes: + +- **`mode="human"`** (default): Display in a window +- **`mode="rgb_array"`**: Return numpy array for custom processing + +```python +# Get frame as numpy array +frame = env.render(mode="rgb_array") +print(frame.shape) # e.g., (240, 320, 3) +``` + +## Development & Testing + +### Local Development + +Install dependencies and run locally without Docker: + +```bash +# Install the environment in development mode +cd src/envs/doom_env +uv pip install -e . + +# Or using pip +pip install -e . + +# Run the server locally +uv run server --host 0.0.0.0 --port 8000 + +# Or using uvicorn directly +uvicorn server.app:app --reload +``` + +### Testing + +Test the environment logic directly: + +```bash +# From the doom_env directory +python3 -c " +from server.doom_env_environment import DoomEnvironment +from models import DoomAction + +env = DoomEnvironment(scenario='basic') +obs = env.reset() +print(f'Initial observation shape: {obs.screen_shape}') + +for i in range(10): + obs = env.step(DoomAction(action_id=1)) + print(f'Step {i}: reward={obs.reward}, done={obs.done}') +" +``` + +### Running the Example Script + +The `example.py` script demonstrates both Docker and local usage with rendering: + +```bash +# Run with Docker (no rendering) +python example.py + +# Run with Docker and rendering +python example.py --render + +# Run locally without Docker +python example.py --local + +# Run locally with rendering (uses native ViZDoom window) +python example.py --local --render + +# Run for more steps +python example.py --local --render --steps 300 +``` + +## Deploying to Hugging Face Spaces + +Deploy your Doom environment to Hugging Face Spaces: + +```bash +# From the doom_env directory +openenv push + +# Or specify options +openenv push --repo-id my-org/doom-env --private +``` + +The `openenv push` command will: +1. Validate the environment setup +2. Prepare for Hugging Face Docker space +3. Upload to Hugging Face + +After deployment, your space will include: +- **Web Interface** at `/web` - Interactive UI +- **API Documentation** at `/docs` - OpenAPI/Swagger +- **Health Check** at `/health` - Monitoring + +The doom-env space is already pushed and available at: [Huggingface Doom OpenEnv](https://huggingface.co/spaces/Crashbandicoote2/doom_env) + +## Project Structure + +``` +doom_env/ +├── .dockerignore # Docker build exclusions +├── __init__.py # Module exports (DoomAction, DoomObservation, DoomEnv) +├── README.md # This file +├── GIF_GENERATION.md # Guide for generating scenario GIFs +├── openenv.yaml # OpenEnv manifest +├── pyproject.toml # Dependencies (vizdoom, numpy, etc.) +├── uv.lock # Locked dependencies +├── client.py # DoomEnv HTTP client +├── models.py # DoomAction and DoomObservation dataclasses +├── example.py # Example usage script +├── generate_gifs.py # Script to generate GIFs of scenarios +├── assets/ # Directory for generated GIFs +│ ├── .gitkeep +│ └── README.md # Assets directory documentation +└── server/ + ├── __init__.py # Server module exports + ├── doom_env_environment.py # Core ViZDoom wrapper + ├── app.py # FastAPI application + ├── requirements.txt # Python dependencies for Docker + └── Dockerfile # Container with ViZDoom dependencies +``` + +## Dependencies + +- **ViZDoom**: Doom-based AI research platform +- **NumPy**: Array operations for screen buffers +- **OpenEnv Core**: Base framework +- **FastAPI/Uvicorn**: HTTP server +- **System libraries**: SDL2, Boost, OpenGL, etc. (handled in Dockerfile) + +All dependencies are defined in `pyproject.toml` and automatically installed during Docker build. + +## Configuration + +### Environment Variables + +The Docker container supports several environment variables for configuration: + +**Web Interface:** +- `ENABLE_WEB_INTERFACE` (default: `true`) - Enable/disable the web UI at `/web` + +**Doom Environment:** +- `DOOM_SCENARIO` (default: `basic`) - Which scenario to load +- `DOOM_SCREEN_RESOLUTION` (default: `RES_160X120`) - Screen resolution +- `DOOM_SCREEN_FORMAT` (default: `RGB24`) - Screen format (RGB24, GRAY8, etc.) +- `DOOM_WINDOW_VISIBLE` (default: `false`) - Show native ViZDoom window + +**Example:** +```bash +docker run -p 8000:8000 \ + -e ENABLE_WEB_INTERFACE=true \ + -e DOOM_SCENARIO=deadly_corridor \ + -e DOOM_SCREEN_RESOLUTION=RES_320X240 \ + doom-env:latest +``` + +## Troubleshooting + +### ViZDoom Installation Issues + +If you encounter issues installing ViZDoom: + +```bash +# Make sure you have system dependencies (Ubuntu/Debian) +sudo apt-get install cmake libboost-all-dev libsdl2-dev libfreetype6-dev + +# Then install ViZDoom +pip install vizdoom +``` + +### Docker Build Issues + +If Docker build fails with ViZDoom dependencies: +- Ensure you have sufficient disk space +- Check that the base image is accessible +- Verify system dependencies in Dockerfile + +### Runtime Errors + +- **"Could not load scenario"**: Check scenario name or path +- **"Invalid action_id"**: Ensure action_id is within valid range +- **Screen buffer issues**: Verify screen format and resolution settings + +## References + +- [ViZDoom Documentation](http://vizdoom.cs.put.edu.pl/) +- [ViZDoom GitHub](https://github.com/mwydmuch/ViZDoom) +- [OpenEnv Documentation](https://github.com/meta-pytorch/OpenEnv) + +## License + +BSD 3-Clause License (see LICENSE file in repository root) + +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⢾⠍⡉⠉⠙⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣴⠾⠿⠽⢷⣶⣤⡀⠀⠀⠀⠀⠀⠀⠀⢀⣟⡟⣠⣿⣶⡀⣷⡻⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠴⡟⡋⡀⠀⣀⣀⠀⠀⠉⠛⣦⡀⠀⠀⠀⠀⠀⠀⢿⣅⣽⣿⣿⣷⣿⣿⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡴⠃⢀⢾⣿⣿⣿⣯⣬⣽⣿⣀⡀⠈⠙⣆⠀⠀⠀⠀⢀⣸⣯⣿⣾⡷⢻⣿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣜⢁⠁⣾⡿⣙⠿⣯⣭⣍⣹⠼⠋⠁⣴⠀⢘⣧⠀⠀⡴⢛⣭⢟⠽⠋⢠⣼⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢻⠘⢸⡿⢷⣬⣧⡀⠀⠀⠀⢀⣤⠾⢿⡇⠘⣿⡆⣸⠛⣿⡿⣟⡀⠀⡾⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣾⣦⣿⣿⡄⠈⢿⢿⣷⣶⡾⠋⠁⠀⣸⠇⡰⠛⢷⣷⣻⡿⠺⣿⣿⠽⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⣀⣀⣀⠀⣴⠏⠀⣿⠙⢻⣿⣄⠈⠀⠸⠀⠉⠀⣠⣾⠟⢀⣧⡇⠀⢽⣿⣿⣬⣼⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⠚⣿⣿⣿⣿⣿⡿⠟⢛⣰⣿⣧⣷⣝⡿⣷⣞⢷⣄⣲⣾⣿⡃⢰⡿⡟⢀⣴⣿⣿⣿⣯⡿⠿⣿⣶⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⠞⢁⣼⣿⣿⣿⡟⠋⠁⣉⣽⣿⣿⣿⣿⣿⣽⣯⣿⡄⠉⠁⢷⣬⣹⣿⣿⣤⡾⠁⣸⣿⣿⡟⠁⠀⠀⢹⣿⣿⣷⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⡷⠞⣫⣾⣿⣿⣿⣧⡀⣤⠀⠈⣻⣿⣿⣿⣿⣿⣿⣿⣷⣖⠀⠘⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠃⠋⠻⢤⣅⡺⢦⡀⠳⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣯⣴⠞⠁⣀⣿⣿⣿⣿⣷⣄⣤⠤⢊⣿⣿⣿⣿⣿⣿⣿⣿⣯⣴⣴⣶⣿⣿⠟⣸⣿⣿⣿⣿⡏⡆⠀⢠⣤⣠⣥⠀⡟⣶⣿⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⣽⡿⣿⣏⡀⠀⠹⣟⣿⡿⣿⣿⣋⣶⣺⡽⣿⣏⣅⠛⠂⠴⠶⠿⠿⠃⠈⠉⠻⣷⣶⣿⣿⣿⣿⡿⠀⣿⡄⠈⣷⣮⠙⢀⡿⠘⢻⣇⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⢸⣧⢻⡶⠀⠀⠘⢿⣿⣿⣿⣿⣿⠋⠉⠀⠀⠉⠻⠿⠶⠶⠶⠦⠴⠞⠛⠷⠗⠈⠛⢿⣿⣿⡿⢁⣼⠯⠄⠀⠀⠀⣠⡞⠁⣠⣾⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⢸⣻⣾⣷⡀⢐⠀⣿⣿⣿⣿⣿⠁⠀⠠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡁⠰⣾⣿⠀⠀⠈⢻⣿⣅⢿⣇⠀⠀⠀⠀⢀⣿⡟⠀⡷⢿⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⣿⡎⣤⣌⡰⣿⣿⣿⣿⣟⠀⠠⠀⢀⡀⠀⠂⠀⠀⠉⠉⠉⠈⠉⠙⢾⣭⡤⠂⠀⠀⠹⣿⣎⣿⣶⣒⣿⣷⣿⣯⣮⡵⣿⣾⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢻⢿⠛⣿⠛⠛⢿⣿⣿⣃⢀⣀⣀⠀⣀⣤⣾⠓⠶⠖⠷⣤⣄⡀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣾⣿⣿⣿⠍⣩⣉⣿⡆⠰⣿⣭⡇⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡸⠾⢴⡇⠀⠀⢸⣿⣿⣯⣭⣿⣿⣿⡿⠛⠛⠛⠛⠛⠛⠛⠟⠻⣷⣶⣴⣶⣮⡴⠫⢾⣿⣿⣟⠉⣹⣿⣿⣿⣿⣷⣄⠸⢿⡇⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⣴⠋⡽⠁⡾⠀⠀⠀⣼⣿⣧⣁⣴⣶⠾⢿⣿⡶⠀⠒⠒⠂⠀⠀⠀⣰⣾⣧⣌⣉⠙⠂⢠⢿⣿⣿⣫⡿⠿⠋⠉⠈⠙⢻⣽⢧⠀⣽⣄⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⢀⣴⡻⣻⣼⣿⣰⠇⠀⠀⠀⠉⣁⣿⣟⢉⣼⣶⣶⡿⠿⣿⡟⠛⠛⠛⣷⣾⢿⣯⣤⣤⡉⠳⡶⢋⡞⣿⣿⣇⠀⠀⠙⠀⠀⠀⢀⣿⣫⠇⣈⣁⣣⡀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⣠⠎⠉⣰⣿⣿⣿⠉⢲⣤⠀⠀⠾⣿⣿⣳⣜⢿⡟⠫⢠⣶⣾⣷⣤⣤⣼⣯⡤⣤⣀⣻⠻⣿⣦⣠⠞⣼⣿⡿⢿⣤⡸⣷⣦⣤⣴⣿⣿⣯⠼⢥⣈⣿⡗⠶⢤⡀⠀⠀ +⠀⠀⠀⠀⢰⡃⠀⣼⣿⣿⣿⣿⣷⣤⣁⣀⣤⣾⣿⣿⣿⣿⣿⣿⠷⣾⣟⣀⣫⣄⣀⣀⣠⣄⠘⢿⡤⠴⣷⡿⠃⠘⡽⣿⣃⠘⣿⣿⣿⣿⣿⣿⣿⠿⡿⠟⠀⠘⣝⢿⡆⠀⠻⣦⡀ +⠀⠀⠀⢀⡏⢀⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⢳⣿⣯⣿⣿⣿⡿⣾⣿⡏⠙⣿⡉⠙⡍⠉⢿⣟⣴⠶⠾⢿⣟⠷⣿⡿⠿⣷⣾⡇⢻⣿⣿⡏⡴⠞⠻⣞⡍⠙⢫⣿⣧⠀⠀⠘⠃ +⠀⠀⢀⣾⢿⡾⢷⣿⣿⠋⣿⣿⣿⣿⣿⣿⣿⡗⣼⢿⣿⣿⠘⣿⣿⣷⣼⣣⣶⠾⠿⠛⠶⣦⠚⣠⣴⣿⣿⠋⢰⣼⣯⠁⠐⢺⣿⣿⣮⣿⣿⣿⡟⠂⢀⣽⡓⡀⠒⢹⣿⠇⠀⣤⡀ +⠀⠀⣿⠿⣾⣳⣼⣏⠛⠛⢿⣯⣶⣿⢋⣼⣿⢱⣟⣷⣮⠻⣷⠘⠿⣿⣭⣉⡉⣠⣤⣤⣄⣉⣉⣁⣾⡿⠟⣠⣾⡏⣡⠎⠀⢸⣿⡌⣿⣿⣿⣿⣟⡂⠠⢿⡅⢨⡏⣾⣟⠀⠀⠈⠁ +⠀⢸⡿⠓⢀⣿⣿⣿⡷⣦⣼⠟⣹⡵⠛⢳⢟⣾⣿⣿⡿⠀⣿⠄⡀⣿⣯⠙⣿⡟⠛⢛⠛⣿⣿⡏⢉⡇⠀⢯⣿⡇⡅⢴⠀⢸⣾⡇⠸⣟⠹⣿⢿⡏⢰⣿⣆⣈⠁⣽⣿⠀⠀⠀⠀ +⢀⡖⠘⠃⢠⣿⣯⡟⠻⣿⣻⡟⠃⠀⠀⠸⣿⢿⣿⣿⣿⣾⣿⣿⣿⣿⣿⣧⢸⣧⣤⣭⣤⣿⣿⡔⢿⣿⡿⣿⣿⣿⣷⣤⣠⣿⣿⠃⠀⣿⣇⣿⣿⣷⣿⣿⠿⢽⣷⣩⣿⠀⠀⠀⠀ +⣾⠁⣠⠹⣿⣿⡟⠻⣶⣿⢻⡇⠀⠀⠀⠀⠈⢹⡿⣿⣿⣿⣿⢟⣟⢿⢿⣿⣿⡷⠶⠶⠶⠈⢯⡻⡄⢻⣿⢀⠙⢿⣿⣿⣷⡟⠁⢀⣴⢟⣺⣿⣿⣿⣥⣽⣶⣄⣈⣿⣿⠀⠀⠀⠀ +⣭⠎⠿⢠⡟⢿⣿⣷⣽⣿⣼⡇⠀⠀⠀⠀⢠⣿⢿⡛⢿⡿⣿⡾⣿⡇⢠⣿⣿⡇⠀⠀⠀⠀⣈⢻⡖⢸⣿⢿⣾⢏⠟⠛⢿⣧⣀⣸⣴⡿⢻⣿⣻⣍⠉⣉⠛⣛⠛⠛⢿⡷⠀⠀⢀ +⢳⣶⠖⠈⢿⣿⣛⠹⣿⣿⢸⡃⠀⠀⠀⣠⠟⣩⠞⠀⠈⣿⡟⣵⡿⠃⣼⣿⣿⠁⠐⠀⠘⠃⠉⣸⣇⠀⠹⣦⢻⣟⠀⠀⠀⠹⣿⣴⣯⣼⣿⣿⣿⣿⡄⣿⡀⢿⣰⡇⢸⡇⠀⠠⠋ +⠸⣹⡶⠀⢸⣿⣿⣿⣷⣛⢻⡇⠀⠀⢠⡷⠃⠁⠀⠀⠀⣿⠸⣿⠀⠠⣿⣿⣧⡀⠀⠀⠀⠀⢰⣿⣄⠁⠀⣹⢦⣿⣦⠀⠀⠀⣿⣿⣿⡏⣿⡏⡛⠟⢲⣶⢶⣾⣷⡭⣸⡴⠊⠀⠀ +⠀⢹⡄⣄⡘⣿⣿⣿⣿⠹⡿⠁⠀⠀⣿⠇⠀⠀⠀⠀⡶⠘⡇⣿⡃⠂⣻⣿⣿⣷⡄⠀⠀⠀⢸⣿⣝⡓⢰⣿⣾⡏⣿⣦⠀⠀⢹⣾⣿⡎⢰⣷⣓⠀⣼⣿⢸⣿⢹⡆⢿⠇⠀⠀⠀ +⠀⠀⠙⠻⣿⣿⠧⠭⠭⠟⠁⠀⠀⣸⡽⢐⠀⠀⠀⢸⣇⣸⡷⣿⠃⠀⢿⣿⣿⣿⣿⣿⣷⣦⣿⣿⣯⡟⢺⣿⣿⣇⣸⡿⡇⠀⢀⡟⣿⡧⢸⣷⡌⢀⣿⣿⣼⣿⠮⣿⠋⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠈⠁⠀⠀⠀⠀⠀⢀⣟⣷⡿⠀⠀⠀⠀⠉⢸⡇⡷⠀⢀⠈⠻⣿⣿⢿⣿⢿⣿⣿⣿⣿⣷⣾⣿⣿⡇⠉⠀⠀⠀⢸⡇⢸⣿⡾⡿⣧⣼⣿⠵⣿⣇⡾⠁⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣾⣼⡛⢸⡄⠀⠀⠀⢸⣧⢳⣀⠀⠀⠀⣿⢋⡟⠈⢧⢻⣿⣿⣿⣿⣿⣿⣿⣷⡀⠀⠀⠀⠈⡇⠀⢯⡇⠀⠉⠙⠙⠉⠉⠋⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⢹⡸⣷⠀⠀⠀⠄⠻⢷⣄⣀⢀⣼⣣⠟⠀⠀⠈⢣⠹⣿⣿⣿⣿⣿⣿⠿⢷⡄⠀⠀⠀⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⡈⢁⣽⣷⡆⠀⠀⠀⠀⢈⣽⣿⡿⠃⠀⠀⠀⠀⠀⠙⣌⢻⣿⣿⣿⣿⠀⠈⢿⣦⠓⠀⠀⠀⣸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⠚⢿⣷⣿⣿⣯⣻⡄⠀⠀⢀⣾⠟⡿⠁⠀⠀⠀⠀⠀⠀⠀⠈⢦⡻⣿⣿⣷⡀⢠⣾⣫⡿⣬⡃⠆⠛⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣻⣷⢾⣟⠛⠁⠉⢻⣿⣆⣠⡾⢿⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⣇⣸⣿⣿⣾⣾⣿⠇⠀⠈⢙⡟⠿⢻⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣫⡾⠿⣦⣀⣀⣠⡿⢿⣏⡴⣿⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⣿⣿⣿⣿⣤⣤⣤⡞⠁⢂⣹⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⣉⣿⡳⠀⠀⠈⠁⠀⠀⠈⢿⡄⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⣿⢿⣿⣿⣿⣷⣄⠀⠀⠀⢀⡀⠘⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣰⣷⣻⣿⡟⠶⠶⠤⠤⠀⠀⠀⣸⣿⡿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⣿⣿⣿⣿⣿⣿⣶⣖⣾⠭⡁⠈⢿⣳⣦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⢰⣟⣿⣽⠋⣿⠃⢤⣭⣭⠀⠀⠀⣠⣟⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⡿⣿⣿⣿⣿⣯⡥⠶⠀⣛⠀⢶⡿⡬⣷⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⣶⡿⢱⣿⣰⡿⠿⠶⢭⣦⠀⠀⣰⡿⢁⢿⣾⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⣿⣢⣿⣿⣿⣿⣷⣶⠖⠛⠙⢷⣌⡉⠹⣷⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⢰⣿⣗⣿⣿⣿⡀⣶⣶⠀⣹⣷⣾⣿⣷⡼⣯⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⢿⣿⣿⣿⣿⣿⣿⣿⣗⣼⠄⠀⣿⣷⣀⣿⢷⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠈⢹⣿⡿⢻⣿⣷⣽⣏⣰⣿⣿⣿⣷⣶⣧⢹⣷⡟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣭⠿⣿⣿⣿⣿⣿⣿⣿⣿⣥⣤⣾⠏⠻⡇⣿⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⢿⣿⣿⣟⠻⠿⠿⠛⠹⣿⣿⣿⣿⣾⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡇⣻⣿⣿⣿⣿⣿⡍⠛⠛⠋⠁⠀⣀⢿⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⢸⡿⣿⣿⣿⣿⡗⠓⣤⠀⢀⣤⣿⢃⣸⠟⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⢿⠻⣿⣿⣿⣿⣿⣏⣹⣧⢀⣾⡉⢸⣿⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⠀⣼⡿⣿⣿⣿⣛⠁⠘⡋⠙⣋⣥⣿⢾⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢣⡜⣿⣿⣿⣿⡏⠛⢩⡉⠀⡛⢸⣿⢷⠀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⢀⣿⣷⣿⣿⣿⣯⣤⡤⡒⣛⣭⣭⢾⡿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⣿⢹⣿⣿⣿⣿⡦⠼⣷⠚⣩⠏⠹⣯⡀⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⠀⣿⡅⢠⣿⣿⣿⣧⣤⠾⠟⢛⣫⡵⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣳⢿⣿⣿⣿⣿⡶⣿⡞⠋⠀⠀⢻⣧⠀⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⢰⣿⢡⢿⣿⣿⣿⣀⡀⠚⣠⣼⠁⢀⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣇⣼⣿⣿⣿⣿⣇⣼⣷⣶⣿⠟⠀⣿⣇⠀⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⠀⣞⠇⣶⣷⣬⣭⣉⣛⢛⣛⠉⣩⡷⢾⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡯⢽⣿⣿⣿⣿⣟⣉⣩⣤⡤⠶⠂⠸⣾⡄⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⠀⣾⣿⡷⣿⣽⣾⣟⣿⣭⠈⠁⠀⣿⣠⣼⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣷⢦⣿⣿⣿⣿⣿⣯⣁⣾⣷⣶⣿⠣⣷⣵⠀⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⢀⣾⢻⠤⠟⠓⠚⠻⢧⣀⠀⠀⠀⠙⣿⣿⣯⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢻⣿⣿⢡⠿⠛⠋⠉⠩⠀⠀⠀⠒⠄⠞⣦⠀⠀⠀⠀⠀ +⠀⠀⠀⠀⢸⣷⣴⢞⣏⣀⠀⡀⠀⣹⣦⠾⠟⢂⡍⠻⣷⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢨⠿⢛⣿⡶⢷⣤⣄⣀⣀⡀⢠⣴⣀⠠⡼⣿⡁⠀⠀⠀⠀ +⠀⠀⠀⠀⢘⡃⢰⠀⡀⠀⠀⢀⡀⠀⠀⠈⠀⠀⢩⠈⣽⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣼⣿⣿⡀⣀⠒⣿⣿⠇⠀⠀⠀⠀⡀⡇⠘⣿⡀⠀⠀⠀ +⠀⠀⠀⠀⠀⡇⢸⠈⠁⠀⠀⢸⡇⠀⡇⠀⢖⣔⣾⣾⡋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠑⣟⢿⣀⣿⣾⢿⣿⡇⠈⠁⠀⠀⠀⣿⣤⣧⠇⠀⠀⠀ diff --git a/src/envs/doom_env/__init__.py b/src/envs/doom_env/__init__.py new file mode 100644 index 000000000..316308451 --- /dev/null +++ b/src/envs/doom_env/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Doom Env Environment - A full-featured ViZDoom integration for visual reinforcement learning (RL) research.""" + +from .client import DoomEnv +from .models import DoomAction, DoomObservation + +__all__ = ["DoomAction", "DoomObservation", "DoomEnv"] + diff --git a/src/envs/doom_env/assets/.gitkeep b/src/envs/doom_env/assets/.gitkeep new file mode 100644 index 000000000..a1d647328 --- /dev/null +++ b/src/envs/doom_env/assets/.gitkeep @@ -0,0 +1,2 @@ +# This file ensures the assets directory is tracked by git +# GIFs will be generated using generate_gifs.py diff --git a/src/envs/doom_env/assets/README.md b/src/envs/doom_env/assets/README.md new file mode 100644 index 000000000..d59fedcce --- /dev/null +++ b/src/envs/doom_env/assets/README.md @@ -0,0 +1,39 @@ +# Doom Environment GIF Generation + +This directory contains GIFs showcasing different ViZDoom scenarios. + +## Generating GIFs + +To generate GIFs for the scenarios: + +```bash +# Install dependencies +pip install numpy imageio vizdoom + +# Generate GIFs for default scenarios +cd /path/to/OpenEnv/src/envs/doom_env +python generate_gifs.py + +# Or generate specific scenarios +python generate_gifs.py --scenario basic deadly_corridor + +# Custom settings +python generate_gifs.py --frames 150 --fps 20 --resolution RES_640X480 +``` + +## Generated GIFs + +The script will generate the following GIFs: + +- `basic.gif` - Basic scenario with simple movement and shooting +- `deadly_corridor.gif` - Navigate a corridor while avoiding/killing monsters +- `defend_the_center.gif` - Defend the center position against enemies +- `health_gathering.gif` - Collect health packs to survive + +## Usage in README + +These GIFs are embedded in the main README.md file to showcase the environment's capabilities. + +## File Sizes + +GIFs are optimized for documentation (80 frames @ 12 fps, 320x240 resolution) to keep file sizes reasonable while showing representative gameplay. diff --git a/src/envs/doom_env/assets/basic.gif b/src/envs/doom_env/assets/basic.gif new file mode 100644 index 000000000..6622d7c9a Binary files /dev/null and b/src/envs/doom_env/assets/basic.gif differ diff --git a/src/envs/doom_env/assets/deadly_corridor.gif b/src/envs/doom_env/assets/deadly_corridor.gif new file mode 100644 index 000000000..1f1c3477f Binary files /dev/null and b/src/envs/doom_env/assets/deadly_corridor.gif differ diff --git a/src/envs/doom_env/assets/defend_the_center.gif b/src/envs/doom_env/assets/defend_the_center.gif new file mode 100644 index 000000000..feeffce0c Binary files /dev/null and b/src/envs/doom_env/assets/defend_the_center.gif differ diff --git a/src/envs/doom_env/assets/defend_the_line.gif b/src/envs/doom_env/assets/defend_the_line.gif new file mode 100644 index 000000000..dc75b7d93 Binary files /dev/null and b/src/envs/doom_env/assets/defend_the_line.gif differ diff --git a/src/envs/doom_env/assets/demo.html b/src/envs/doom_env/assets/demo.html new file mode 100644 index 000000000..453b99f85 --- /dev/null +++ b/src/envs/doom_env/assets/demo.html @@ -0,0 +1,866 @@ + + + + + + Doom Environment for OpenEnv - Visual RL Training + + + + +
+
+ + +
+

🎮 DOOM ENV

+

Visual Reinforcement Learning for OpenEnv

+

"Training agents is more fun when they're fighting demons"

+
+ + +
+ Doom Slayer at OpenEnv School +
+ + +
+
+
0
+
Scenarios
+
+
+
0
+
Vision Model Types
+
+
+
0
+
Action Types
+
+
+
0
+
Years of Doom Legacy
+
+
+ + +
+
+
Basic Scenario
+
Deadly Corridor
+
Defend the Center
+
Health Gathering
+
Defend the Line
+
My Way Home
+
Predict Position
+
Take Cover
+ +
Basic Scenario
+
Deadly Corridor
+
Defend the Center
+
Health Gathering
+
Defend the Line
+
My Way Home
+
Predict Position
+
Take Cover
+
+
+ + +
+

🧠 Vision Models Supported

+
+
+ CNN + Convolutional Networks +
+
+ ViT + Vision Transformers +
+
+ VLM + Vision-Language Models +
+
+
+ + +
+

⚡ Key Features

+
+
+ 👁️ +

Visual Observations

+

RGB or grayscale screen buffers with configurable resolution

+
+
+ ❤️ +

Game Variables

+

Health, ammo, kills, armor - perfect for reward shaping

+
+
+ 🐳 +

Docker Ready

+

Fully containerized with web interface for debugging

+
+
+ 🎯 +

Flexible Actions

+

Discrete action IDs or button combinations

+
+
+ 🔧 +

OpenEnv Compatible

+

Works with TRL, torchforge, and SkyRL

+
+
+
+ + +
+

💻 Simple API

+ +
+
+
+ + + +
+ doom_example.py +
+ 
from envs.doom_env import DoomAction, DoomEnv
+import numpy as np
+
+# Deploy with Docker - fully isolated
+env = DoomEnv.from_docker_image("doom-env:latest")
+
+# Standard RL interface
+obs = env.reset()
+print(f"Screen shape: {obs.observation.screen_shape}")
+
+# Take actions and get visual observations
+for step in range(1000):
+    obs = env.step(DoomAction(action_id=1))
+
+    # Reshape screen buffer for vision models
+    frame = np.array(obs.observation.screen_buffer)
+    frame = frame.reshape(obs.observation.screen_shape)
+
+    # Feed to CNN/ViT/VLM
+    if obs.observation.done:
+        break
+
+env.close()
+
+ +
+
+
+ + + +
+ vision_model_integration.py +
+ 
import torch
+from torchvision import transforms
+
+# Process observations for different vision models
+obs = env.reset()
+screen = np.array(obs.observation.screen_buffer)
+screen = screen.reshape(obs.observation.screen_shape)
+
+# For CNN: Direct pixel input [H, W, C] → [C, H, W]
+cnn_input = torch.tensor(screen).permute(2, 0, 1).float() / 255.0
+
+# For ViT: Resize to standard patch size
+vit_transform = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+vit_input = vit_transform(screen)
+
+# For VLM: Combine with task description
+task_prompt = "Navigate the corridor and eliminate enemies"
+vlm_input = (screen, task_prompt)
+
+
+ + +
+

🎮 Scenarios Gallery

+
+
+ Basic Scenario +
+

Basic

+

Learn fundamental movement and shooting mechanics. Perfect starting point for RL agents.

+
+
+
+ Deadly Corridor +
+

Deadly Corridor

+

Navigate through a dangerous corridor while avoiding or eliminating monsters.

+
+
+
+ Defend the Center +
+

Defend the Center

+

Survive waves of attacking monsters from all directions. Test defensive strategies.

+
+
+
+ Health Gathering +
+

Health Gathering

+

Resource collection under time pressure. Learn to prioritize and navigate efficiently.

+
+
+
+ My Way Home +
+

My Way Home

+

Pure navigation challenge. Find the exit in a maze-like environment.

+
+
+
+ Predict Position +
+

Predict Position

+

Anticipate and hit moving targets. Develops temporal reasoning skills.

+
+
+
+ Defend the Line +
+

Defend the Line

+

Defend a line against incoming monsters. Test strategic positioning.

+
+
+
+ Take Cover +
+

Take Cover

+

Learn defensive strategies. Avoid enemy fire by using cover effectively.

+
+
+
+
+ + +
+

🚀 Quick Start

+
+
+ git clone -b alidev_doom_env_01 https://github.com/AlirezaShamsoshoara/OpenEnv.git + +
+
+ cd OpenEnv +
+
+ docker pull vmvm-registry.fbinfra.net/hackathon/pe_hackathon/alisol/doom-env:latest +
+
+ python examples/doom_example.py --docker --render +
+
+
+ + + + + + + diff --git a/src/envs/doom_env/assets/doom_slayer_at_openEnv_school.png b/src/envs/doom_env/assets/doom_slayer_at_openEnv_school.png new file mode 100644 index 000000000..27eb67a30 Binary files /dev/null and b/src/envs/doom_env/assets/doom_slayer_at_openEnv_school.png differ diff --git a/src/envs/doom_env/assets/health_gathering.gif b/src/envs/doom_env/assets/health_gathering.gif new file mode 100644 index 000000000..224ee777c Binary files /dev/null and b/src/envs/doom_env/assets/health_gathering.gif differ diff --git a/src/envs/doom_env/assets/my_way_home.gif b/src/envs/doom_env/assets/my_way_home.gif new file mode 100644 index 000000000..fd7f3b71a Binary files /dev/null and b/src/envs/doom_env/assets/my_way_home.gif differ diff --git a/src/envs/doom_env/assets/predict_position.gif b/src/envs/doom_env/assets/predict_position.gif new file mode 100644 index 000000000..f01db0223 Binary files /dev/null and b/src/envs/doom_env/assets/predict_position.gif differ diff --git a/src/envs/doom_env/assets/take_cover.gif b/src/envs/doom_env/assets/take_cover.gif new file mode 100644 index 000000000..377c0dc74 Binary files /dev/null and b/src/envs/doom_env/assets/take_cover.gif differ diff --git a/src/envs/doom_env/client.py b/src/envs/doom_env/client.py new file mode 100644 index 000000000..39e6a0931 --- /dev/null +++ b/src/envs/doom_env/client.py @@ -0,0 +1,243 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Doom Environment HTTP Client. + +This module provides the client for connecting to a Doom Environment server +over HTTP. +""" + +from typing import Dict, Optional + +import numpy as np + +from openenv_core.client_types import StepResult +from openenv_core.env_server.types import State +from openenv_core.http_env_client import HTTPEnvClient + +from .models import DoomAction, DoomObservation + + +class DoomEnv(HTTPEnvClient[DoomAction, DoomObservation]): + """ + HTTP client for the Doom Environment. + + This client connects to a DoomEnvironment HTTP server and provides + methods to interact with it: reset(), step(), and state access. + + The Doom environment wraps ViZDoom scenarios for visual RL research. + + Example: + >>> # Connect to a running server + >>> client = DoomEnv(base_url="http://localhost:8000") + >>> result = client.reset() + >>> print(result.observation.screen_shape) + >>> + >>> # Take an action + >>> result = client.step(DoomAction(action_id=2)) + >>> print(result.observation.reward, result.observation.done) + + Example with Docker: + >>> # Automatically start container and connect + >>> client = DoomEnv.from_docker_image("doom-env:latest") + >>> result = client.reset() + >>> result = client.step(DoomAction(action_id=0)) + >>> client.close() + + Example with rendering: + >>> client = DoomEnv.from_docker_image("doom-env:latest") + >>> result = client.reset() + >>> for _ in range(100): + >>> result = client.step(DoomAction(action_id=1)) + >>> client.render() # Display the game + >>> client.close() + """ + + def __init__(self, *args, **kwargs): + """Initialize DoomEnv client.""" + super().__init__(*args, **kwargs) + self._render_window = None + self._last_observation = None + + def _step_payload(self, action: DoomAction) -> Dict: + """ + Convert DoomAction to JSON payload for step request. + + Args: + action: DoomAction instance + + Returns: + Dictionary representation suitable for JSON encoding + """ + # Use dataclasses.asdict to ensure proper serialization + from dataclasses import asdict + + # Convert to dict and filter out None values + action_dict = asdict(action) + + # Convert numpy types to native Python types for JSON serialization + result = {} + for k, v in action_dict.items(): + if v is None: + continue + # Handle numpy integers and floats + if hasattr(v, 'item'): # numpy scalar types + result[k] = v.item() + # Handle numpy arrays/lists + elif isinstance(v, (list, tuple)): + result[k] = [x.item() if hasattr(x, 'item') else x for x in v] + else: + result[k] = v + + return result + + def _parse_result(self, payload: Dict) -> StepResult[DoomObservation]: + """ + Parse server response into StepResult[DoomObservation]. + + Args: + payload: JSON response from server + + Returns: + StepResult with DoomObservation + """ + obs_data = payload.get("observation", {}) + observation = DoomObservation( + screen_buffer=obs_data.get("screen_buffer", []), + screen_shape=obs_data.get("screen_shape", [120, 160, 3]), + game_variables=obs_data.get("game_variables"), + available_actions=obs_data.get("available_actions"), + episode_finished=obs_data.get("episode_finished", False), + done=payload.get("done", False), + reward=payload.get("reward"), + metadata=obs_data.get("metadata", {}), + ) + + # Store for rendering + self._last_observation = observation + + return StepResult( + observation=observation, + reward=payload.get("reward"), + done=payload.get("done", False), + ) + + def _parse_state(self, payload: Dict) -> State: + """ + Parse server response into State object. + + Args: + payload: JSON response from /state endpoint + + Returns: + State object with episode_id and step_count + """ + return State( + episode_id=payload.get("episode_id"), + step_count=payload.get("step_count", 0), + ) + + def render(self, mode: str = "human") -> Optional[np.ndarray]: + """ + Render the current observation. + + Args: + mode: Render mode - "human" for window display, "rgb_array" for array return. + + Returns: + RGB array if mode is "rgb_array", None otherwise. + """ + if self._last_observation is None: + print("Warning: No observation to render. Call reset() or step() first.") + return None + + # Get screen from observation + screen_buffer = self._last_observation.screen_buffer + screen_shape = self._last_observation.screen_shape + + if not screen_buffer or not screen_shape: + return None + + # Reshape screen buffer to original dimensions + screen = np.array(screen_buffer, dtype=np.uint8).reshape(screen_shape) + + if mode == "rgb_array": + return screen + elif mode == "human": + # Display using cv2 or matplotlib + try: + import cv2 + + # Create window if it doesn't exist + if self._render_window is None: + self._render_window = "ViZDoom - Doom Environment" + cv2.namedWindow(self._render_window, cv2.WINDOW_NORMAL) + + # Convert to BGR for OpenCV (if RGB) + if len(screen.shape) == 3 and screen.shape[2] == 3: + screen_bgr = cv2.cvtColor(screen, cv2.COLOR_RGB2BGR) + else: + screen_bgr = screen + + # Display + cv2.imshow(self._render_window, screen_bgr) + cv2.waitKey(1) + + except ImportError: + # Fallback to matplotlib + try: + import matplotlib.pyplot as plt + + if self._render_window is None: + plt.ion() + self._render_window = plt.figure(figsize=(8, 6)) + self._render_window.canvas.manager.set_window_title( + "ViZDoom - Doom Environment" + ) + + plt.clf() + if len(screen.shape) == 3: + plt.imshow(screen) + else: + plt.imshow(screen, cmap="gray") + plt.axis("off") + plt.pause(0.001) + + except ImportError: + print( + "Warning: Neither cv2 nor matplotlib available for rendering. " + "Install with: pip install opencv-python or pip install matplotlib" + ) + return None + else: + raise ValueError( + f"Invalid render mode: {mode}. Use 'human' or 'rgb_array'." + ) + + def close(self) -> None: + """Close the environment and clean up resources.""" + # Close render window if it exists + if self._render_window is not None: + try: + import cv2 + + cv2.destroyAllWindows() + except ImportError: + try: + import matplotlib.pyplot as plt + + plt.close("all") + except ImportError: + # Neither cv2 nor matplotlib is available for cleanup. + print( + "Warning: Could not clean up render windows because neither cv2 nor matplotlib is available. " + "Install with: pip install opencv-python or pip install matplotlib" + ) + self._render_window = None + + # Call parent close + super().close() diff --git a/src/envs/doom_env/generate_gifs.py b/src/envs/doom_env/generate_gifs.py new file mode 100755 index 000000000..a084873e7 --- /dev/null +++ b/src/envs/doom_env/generate_gifs.py @@ -0,0 +1,247 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Generate GIFs of different Doom scenarios for documentation. + +This script creates animated GIFs showcasing various ViZDoom scenarios +for use in the README and documentation. +""" + +import argparse +import os +from pathlib import Path + +import numpy as np + + +def generate_scenario_gif( + scenario: str, + output_path: str, + num_frames: int = 100, + fps: int = 15, + screen_resolution: str = "RES_320X240", +): + """ + Generate a GIF of a Doom scenario. + + Args: + scenario: Name of the scenario to run + output_path: Path to save the GIF + num_frames: Number of frames to capture + fps: Frames per second for the GIF + screen_resolution: Screen resolution to use + """ + try: + import imageio + from models import DoomAction + from server.doom_env_environment import DoomEnvironment + except ImportError as e: + print(f"Error: Missing dependencies. Install with:") + print(f" pip install imageio vizdoom") + print(f"Error details: {e}") + return False + + print(f"Generating GIF for scenario: {scenario}") + print(f" Frames: {num_frames}, FPS: {fps}, Resolution: {screen_resolution}") + + try: + # Create environment + env = DoomEnvironment( + scenario=scenario, + screen_resolution=screen_resolution, + screen_format="RGB24", + window_visible=False, + use_discrete_actions=True, + ) + + # Reset and collect frames + obs = env.reset() + frames = [] + + print(f" Collecting frames...") + for i in range(num_frames): + # Get screen as numpy array + screen = np.array(obs.screen_buffer, dtype=np.uint8).reshape( + obs.screen_shape + ) + frames.append(screen) + + # Take a random action + if obs.available_actions: + # Bias towards movement and shooting actions + available = obs.available_actions + if len(available) > 3: + # Prefer actions 1-7 (movement/shooting) over 0 (no-op) + action_id = np.random.choice(available[1:]) + else: + action_id = np.random.choice(available) + else: + action_id = 0 + + obs = env.step(DoomAction(action_id=action_id)) + + # Reset if episode ends + if obs.done: + print(f" Episode ended at frame {i}, resetting...") + obs = env.reset() + + if (i + 1) % 20 == 0: + print(f" Progress: {i + 1}/{num_frames} frames") + + env.close() + + # Save as GIF + print(f" Saving GIF to: {output_path}") + imageio.mimsave( + output_path, + frames, + fps=fps, + loop=0, # Loop forever + ) + + print(f"✓ Successfully created GIF: {output_path}") + print(f" Size: {os.path.getsize(output_path) / 1024 / 1024:.2f} MB") + return True + + except Exception as e: + print(f"✗ Error generating GIF for {scenario}: {e}") + import traceback + + traceback.print_exc() + return False + + +def main(): + """Main entry point.""" + parser = argparse.ArgumentParser( + description="Generate GIFs of Doom scenarios", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Generate GIFs for all default scenarios + python generate_gifs.py + + # Generate GIF for a specific scenario + python generate_gifs.py --scenario basic + + # Generate with custom settings + python generate_gifs.py --scenario deadly_corridor --frames 150 --fps 20 + + # Generate for multiple scenarios + python generate_gifs.py --scenario basic deadly_corridor defend_the_center + +Available scenarios: + - basic: Simple scenario with basic movement and shooting + - deadly_corridor: Navigate a corridor avoiding/killing monsters + - defend_the_center: Defend the center position + - defend_the_line: Defend a line against monsters + - health_gathering: Collect health packs to survive + - my_way_home: Navigate to a specific location + - predict_position: Predict object positions + - take_cover: Learn to take cover from enemy fire + """, + ) + + parser.add_argument( + "--scenario", + "-s", + nargs="+", + default=[ + "basic", + "deadly_corridor", + "defend_the_center", + "health_gathering", + "my_way_home", + "predict_position", + "take_cover", + "defend_the_line", + ], + help="Scenario(s) to generate GIFs for", + ) + parser.add_argument( + "--output-dir", + "-o", + default="assets", + help="Output directory for GIFs (default: assets)", + ) + parser.add_argument( + "--frames", + "-f", + type=int, + default=100, + help="Number of frames to capture (default: 100)", + ) + parser.add_argument( + "--fps", type=int, default=15, help="Frames per second (default: 15)" + ) + parser.add_argument( + "--resolution", + "-r", + default="RES_320X240", + choices=[ + "RES_160X120", + "RES_320X240", + "RES_640X480", + "RES_800X600", + "RES_1024X768", + ], + help="Screen resolution (default: RES_320X240)", + ) + + args = parser.parse_args() + + # Create output directory + output_dir = Path(args.output_dir) + output_dir.mkdir(exist_ok=True, parents=True) + + print(f"\n{'='*60}") + print(f"Doom Scenario GIF Generator") + print(f"{'='*60}\n") + print(f"Output directory: {output_dir.absolute()}") + print(f"Scenarios: {', '.join(args.scenario)}") + print(f"Settings: {args.frames} frames @ {args.fps} fps, {args.resolution}") + print() + + # Generate GIFs for each scenario + results = {} + for scenario in args.scenario: + output_file = output_dir / f"{scenario}.gif" + + success = generate_scenario_gif( + scenario=scenario, + output_path=str(output_file), + num_frames=args.frames, + fps=args.fps, + screen_resolution=args.resolution, + ) + + results[scenario] = success + print() + + # Print summary + print(f"{'='*60}") + print(f"Summary") + print(f"{'='*60}\n") + + successful = [s for s, success in results.items() if success] + failed = [s for s, success in results.items() if not success] + + print(f"✓ Successful: {len(successful)}/{len(results)}") + for scenario in successful: + print(f" - {scenario}.gif") + + if failed: + print(f"\n✗ Failed: {len(failed)}/{len(results)}") + for scenario in failed: + print(f" - {scenario}") + + print(f"\nGIFs saved to: {output_dir.absolute()}") + + +if __name__ == "__main__": + main() diff --git a/src/envs/doom_env/models.py b/src/envs/doom_env/models.py new file mode 100644 index 000000000..1aaa4c413 --- /dev/null +++ b/src/envs/doom_env/models.py @@ -0,0 +1,67 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Data models for the Doom Environment. + +The doom_env environment wraps ViZDoom for reinforcement learning research. +ViZDoom is a Doom-based AI research platform for visual RL. +""" + +from dataclasses import dataclass +from typing import List, Optional + +from openenv_core.env_server.types import Action, Observation + + +@dataclass(kw_only=True) +class DoomAction(Action): + """ + Action for the Doom environment. + + Actions are specified as a list of button states. Each element corresponds to + a button (e.g., MOVE_LEFT, MOVE_RIGHT, ATTACK, etc.) with value 0 (not pressed) + or 1 (pressed). + + Attributes: + buttons: List of button states (0 or 1). The length and meaning depend on + the available buttons in the scenario. + action_id: Optional pre-defined action ID if using discrete action space. + If provided, this will be used instead of buttons. + + Example: + # Use discrete action (move left) + DoomAction(action_id=0) + + # Use button combination (move forward and shoot) + DoomAction(buttons=[1, 0, 0, 1]) + """ + + buttons: Optional[List[int]] = None + action_id: Optional[int] = None + + +@dataclass(kw_only=True) +class DoomObservation(Observation): + """ + Observation from the Doom environment. + + Contains the screen buffer, game variables, and episode information. + + Attributes: + screen_buffer: Flattened screen pixels as a list of integers. + Shape is [height, width, channels] before flattening. + screen_shape: Original shape of the screen [height, width, channels]. + game_variables: Current values of game variables (health, ammo, etc.). + available_actions: List of available action IDs if using discrete actions. + episode_finished: Whether the episode has ended. + """ + + screen_buffer: List[int] + screen_shape: List[int] + game_variables: Optional[List[float]] = None + available_actions: Optional[List[int]] = None + episode_finished: bool = False diff --git a/src/envs/doom_env/openenv.yaml b/src/envs/doom_env/openenv.yaml new file mode 100644 index 000000000..727dfd423 --- /dev/null +++ b/src/envs/doom_env/openenv.yaml @@ -0,0 +1,7 @@ +spec_version: 1 +name: doom_env +type: space +runtime: fastapi +app: server.app:app +port: 8000 + diff --git a/src/envs/doom_env/pyproject.toml b/src/envs/doom_env/pyproject.toml new file mode 100644 index 000000000..f78243a19 --- /dev/null +++ b/src/envs/doom_env/pyproject.toml @@ -0,0 +1,49 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +[build-system] +requires = ["setuptools>=45", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "openenv-doom_env" +version = "0.1.0" +description = "Doom environment for OpenEnv - wraps ViZDoom for RL research" +requires-python = ">=3.10" +dependencies = [ + # Core OpenEnv dependencies (required for server functionality) + "openenv-core>=0.1.0", + "fastapi>=0.115.0", + "pydantic>=2.0.0", + "uvicorn[standard]>=0.24.0", + "requests>=2.31.0", + # Environment-specific dependencies + "vizdoom>=1.2.0", + "numpy>=1.19.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=8.0.0", + "pytest-cov>=4.0.0", +] +rendering = [ + "opencv-python>=4.5.0", + "matplotlib>=3.3.0", +] +gif_generation = [ + "imageio>=2.9.0", +] + +[project.scripts] +# Server entry point - enables running via: uv run --project . server +# or: python -m doom_env.server.app +server = "doom_env.server.app:main" + +[tool.setuptools] +include-package-data = true +packages = ["doom_env", "doom_env.server"] +package-dir = { "doom_env" = ".", "doom_env.server" = "server" } diff --git a/src/envs/doom_env/pytest.ini b/src/envs/doom_env/pytest.ini new file mode 100644 index 000000000..6dda233bd --- /dev/null +++ b/src/envs/doom_env/pytest.ini @@ -0,0 +1,26 @@ +[pytest] +# Test discovery patterns +python_files = test_*.py +python_classes = Test* +python_functions = test_* + +# Test paths +testpaths = tests + +# Output options +addopts = + -v + --strict-markers + --tb=short + --disable-warnings + +# Markers +markers = + slow: marks tests as slow (deselect with '-m "not slow"') + requires_server: marks tests that require a running server + requires_vizdoom: marks tests that require ViZDoom installed + requires_docker: marks tests that require Docker + requires_display: marks tests that require display/X11 + +# Minimum Python version +minversion = 3.10 diff --git a/src/envs/doom_env/server/Dockerfile b/src/envs/doom_env/server/Dockerfile new file mode 100644 index 000000000..504cfd60f --- /dev/null +++ b/src/envs/doom_env/server/Dockerfile @@ -0,0 +1,82 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# Dockerfile for Doom Environment +# This image provides ViZDoom for visual RL research with OpenEnv +# +# This Dockerfile works for both local builds and HuggingFace Spaces deployment: +# - Local build from project root: docker build -t doom-env:latest -f src/envs/doom_env/server/Dockerfile src/envs/doom_env +# - HuggingFace: Automatically deployed via `openenv push` +# +# Run with web interface: +# docker run -p 8000:8000 -e ENABLE_WEB_INTERFACE=true doom-env:latest + +FROM python:3.11-slim + +# Set metadata +LABEL maintainer="OpenEnv Team" +LABEL description="Doom RL Environment with ViZDoom and OpenEnv" +LABEL org.opencontainers.image.source="https://github.com/meta-pytorch/OpenEnv" + +# Set working directory +WORKDIR /app/env + +# Install system dependencies +# - curl: for healthcheck +# - ViZDoom build dependencies: build-essential, cmake, boost, SDL2, etc. +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + curl \ + ca-certificates \ + build-essential \ + cmake \ + libboost-all-dev \ + libsdl2-dev \ + libfreetype6-dev \ + libgl1-mesa-dev \ + libglu1-mesa-dev \ + libpng-dev \ + libjpeg-dev \ + libbz2-dev \ + libfluidsynth-dev \ + libgme-dev \ + libopenal-dev \ + zlib1g-dev \ + timidity \ + tar \ + nasm && \ + rm -rf /var/lib/apt/lists/* + +# Set environment variables +ENV PYTHONUNBUFFERED=1 + +# Copy environment files +COPY . . + +# Install doom_env and all its dependencies from pyproject.toml +# This includes: openenv-core, fastapi, uvicorn, pydantic, requests, vizdoom, numpy +RUN pip install --no-cache-dir -e . + +# Doom-specific environment variables (can be overridden at runtime) +ENV DOOM_SCENARIO=basic +ENV DOOM_SCREEN_RESOLUTION=RES_160X120 +ENV DOOM_SCREEN_FORMAT=RGB24 +ENV DOOM_WINDOW_VISIBLE=false + +# Enable web interface by default (set to false to disable) +ENV ENABLE_WEB_INTERFACE=true + +# Expose port +EXPOSE 8000 + +# Health check +HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ + CMD curl -f http://localhost:8000/health || exit 1 + +# Run the FastAPI server +# Web interface will be available at /web if ENABLE_WEB_INTERFACE=true +# API documentation available at /docs +CMD ["uvicorn", "doom_env.server.app:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/src/envs/doom_env/server/__init__.py b/src/envs/doom_env/server/__init__.py new file mode 100644 index 000000000..9bc85f9e2 --- /dev/null +++ b/src/envs/doom_env/server/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Doom Env environment server components.""" + +from .doom_env_environment import DoomEnvironment + +__all__ = ["DoomEnvironment"] + diff --git a/src/envs/doom_env/server/app.py b/src/envs/doom_env/server/app.py new file mode 100644 index 000000000..4074d6688 --- /dev/null +++ b/src/envs/doom_env/server/app.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +FastAPI application for the Doom Env Environment. + +This module creates an HTTP server that exposes the DoomEnvironment +over HTTP endpoints, making it compatible with HTTPEnvClient. + +Usage: + # Development (with auto-reload): + uvicorn server.app:app --reload --host 0.0.0.0 --port 8000 + + # Production: + uvicorn server.app:app --host 0.0.0.0 --port 8000 --workers 4 + + # Or run directly: + python -m server.app +""" + +try: + from openenv_core.env_server.http_server import create_app +except Exception as e: # pragma: no cover + raise ImportError( + "openenv_core is required for the web interface. Install dependencies with '\n uv sync\n'" + ) from e + +try: + from ..models import DoomAction, DoomObservation + from .doom_env_environment import DoomEnvironment +except ImportError: + from doom_env.server.doom_env_environment import DoomEnvironment + from doom_env.models import DoomAction, DoomObservation + +import os + +# Read configuration from environment variables +scenario = os.getenv("DOOM_SCENARIO", "basic") +screen_resolution = os.getenv("DOOM_SCREEN_RESOLUTION", "RES_160X120") +screen_format = os.getenv("DOOM_SCREEN_FORMAT", "RGB24") +window_visible = os.getenv("DOOM_WINDOW_VISIBLE", "false").lower() in ("true", "1", "yes") + +# Create the environment instance with configuration +env = DoomEnvironment( + scenario=scenario, + screen_resolution=screen_resolution, + screen_format=screen_format, + window_visible=window_visible, + use_discrete_actions=True, +) + +# Create the app with web interface and README integration +app = create_app( + env, + DoomAction, + DoomObservation, + env_name="doom_env", +) + + +def main(): + """ + Entry point for direct execution via uv run or python -m. + + This function enables running the server without Docker: + uv run --project . server + python -m envs.doom_env.server.app + openenv serve doom_env + + For production deployments, use uvicorn directly: + uvicorn envs.doom_env.server.app:app --workers 4 + """ + import uvicorn + + print(f"Doom Environment Configuration:") + print(f" Scenario: {scenario}") + print(f" Resolution: {screen_resolution}") + print(f" Format: {screen_format}") + print(f" Window Visible: {window_visible}") + print(f"\nStarting server on http://0.0.0.0:8000") + + uvicorn.run(app, host="0.0.0.0", port=8000) + + +if __name__ == "__main__": + main() diff --git a/src/envs/doom_env/server/doom_env_environment.py b/src/envs/doom_env/server/doom_env_environment.py new file mode 100644 index 000000000..39861e6b9 --- /dev/null +++ b/src/envs/doom_env/server/doom_env_environment.py @@ -0,0 +1,465 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Doom Environment Implementation. + +Wraps ViZDoom for reinforcement learning research with OpenEnv interface. +ViZDoom is a Doom-based AI research platform for visual RL. +""" + +import uuid +from typing import Optional + +try: + from ..models import DoomAction, DoomObservation +except ImportError: + from models import DoomAction, DoomObservation + +from openenv_core.env_server.interfaces import Environment +from openenv_core.env_server.types import State + +# Import ViZDoom +try: + import numpy as np + import vizdoom as vzd +except ImportError as e: + raise ImportError( + "ViZDoom is not installed. " "Please install it with: pip install vizdoom" + ) from e + + +class DoomEnvironment(Environment): + """ + Doom Environment wrapper for OpenEnv. + + This environment wraps ViZDoom scenarios and provides a clean interface + for RL training with visual observations and game variables. + + Args: + scenario: Name of the scenario to load (e.g., "basic", "deadly_corridor"). + Can also be a path to a .cfg file. + screen_resolution: Screen resolution - one of ViZDoom's resolutions. + screen_format: Screen format - "CRCGCB", "RGB24", "GRAY8", etc. + window_visible: Whether to show the game window. + use_discrete_actions: If True, use pre-defined discrete action space. + If False, use continuous button combinations. + + Example: + >>> env = DoomEnvironment("basic") + >>> obs = env.reset() + >>> print(obs.screen_shape) # e.g., [120, 160, 3] + >>> obs = env.step(DoomAction(action_id=0)) # Take action + >>> print(obs.reward, obs.done) + """ + + def __init__( + self, + scenario: str = "basic", + screen_resolution: str = "RES_160X120", + screen_format: str = "RGB24", + window_visible: bool = False, + use_discrete_actions: bool = True, + ): + """Initialize Doom environment.""" + super().__init__() + + self.scenario = scenario + self.screen_resolution = screen_resolution + self.screen_format = screen_format + self.window_visible = window_visible + self.use_discrete_actions = use_discrete_actions + + # Create DoomGame instance + self.game = vzd.DoomGame() + + # Load configuration + self._load_scenario(scenario) + + # Configure visual settings + self._configure_visuals(screen_resolution, screen_format, window_visible) + + # Initialize the game + self.game.init() + + # Get available actions + self.available_buttons = self.game.get_available_buttons() + self.num_buttons = len(self.available_buttons) + + # Create discrete action space if requested + if use_discrete_actions: + self._create_discrete_actions() + else: + self.discrete_actions = None + + # Get screen dimensions + self._update_screen_shape() + + # Initialize state + self._state = State(episode_id=str(uuid.uuid4()), step_count=0) + + # Rendering state + self._render_window = None + + def _load_scenario(self, scenario: str) -> None: + """Load scenario configuration.""" + # Check if scenario is a path to .cfg file + if scenario.endswith(".cfg"): + self.game.load_config(scenario) + else: + # Try to load built-in scenario + try: + # ViZDoom scenarios are typically in scenarios/ directory + scenario_path = vzd.scenarios_path + f"/{scenario}.cfg" + self.game.load_config(scenario_path) + except Exception: + # If built-in scenario not found, try as direct path + try: + self.game.load_config(scenario) + except Exception as e: + raise ValueError( + f"Could not load scenario '{scenario}'. " + f"Provide either a built-in scenario name (e.g., 'basic') " + f"or a path to a .cfg file." + ) from e + + def _configure_visuals( + self, screen_resolution: str, screen_format: str, window_visible: bool + ) -> None: + """Configure visual settings.""" + # Set screen resolution + resolution_map = { + "RES_160X120": vzd.ScreenResolution.RES_160X120, + "RES_320X240": vzd.ScreenResolution.RES_320X240, + "RES_640X480": vzd.ScreenResolution.RES_640X480, + "RES_800X600": vzd.ScreenResolution.RES_800X600, + "RES_1024X768": vzd.ScreenResolution.RES_1024X768, + } + if screen_resolution in resolution_map: + self.game.set_screen_resolution(resolution_map[screen_resolution]) + else: + # Try to use it directly as a ViZDoom resolution + try: + self.game.set_screen_resolution( + getattr(vzd.ScreenResolution, screen_resolution) + ) + except AttributeError: + raise ValueError(f"Invalid screen resolution: {screen_resolution}") + + # Set screen format + format_map = { + "RGB24": vzd.ScreenFormat.RGB24, + "GRAY8": vzd.ScreenFormat.GRAY8, + "CRCGCB": vzd.ScreenFormat.CRCGCB, + "CBCGCR": vzd.ScreenFormat.CBCGCR, + "DOOM_256_COLORS8": vzd.ScreenFormat.DOOM_256_COLORS8, + } + if screen_format in format_map: + self.game.set_screen_format(format_map[screen_format]) + else: + try: + self.game.set_screen_format(getattr(vzd.ScreenFormat, screen_format)) + except AttributeError: + raise ValueError(f"Invalid screen format: {screen_format}") + + # Set window visibility + self.game.set_window_visible(window_visible) + + def _create_discrete_actions(self) -> None: + """ + Create a discrete action space. + + Common actions for most scenarios: + 0: No action + 1: Move left + 2: Move right + 3: Move forward + 4: Move backward + 5: Turn left + 6: Turn right + 7: Attack/Shoot + """ + # Create some basic discrete actions + # Each action is a list of button presses + self.discrete_actions = [] + + # Action 0: No-op + self.discrete_actions.append([0] * self.num_buttons) + + # Create single-button actions + for i in range(self.num_buttons): + action = [0] * self.num_buttons + action[i] = 1 + self.discrete_actions.append(action) + + # You can add more complex combinations here if needed + # For example, move forward + shoot: + # if self.num_buttons >= 2: + # action = [0] * self.num_buttons + # action[0] = 1 # e.g., move forward + # action[1] = 1 # e.g., shoot + # self.discrete_actions.append(action) + + def _update_screen_shape(self) -> None: + """Update screen shape based on current state.""" + if self.game.is_episode_finished(): + # Use default shape if episode is finished + if self.screen_format == "RGB24": + channels = 3 + elif self.screen_format == "GRAY8": + channels = 1 + else: + channels = 3 # default + + # Get resolution from settings + res_str = self.screen_resolution.replace("RES_", "").replace("X", "x") + width, height = map(int, res_str.lower().split("x")) + self.screen_shape = ( + [height, width, channels] if channels > 1 else [height, width] + ) + else: + state = self.game.get_state() + if state is not None and state.screen_buffer is not None: + self.screen_shape = list(state.screen_buffer.shape) + else: + # Fallback + self.screen_shape = [120, 160, 3] + + def reset(self) -> DoomObservation: + """ + Reset the environment and return initial observation. + + Returns: + Initial observation for the agent. + """ + # Start new episode + self.game.new_episode() + + # Reset state tracking + self._state.episode_id = str(uuid.uuid4()) + self._state.step_count = 0 + + # Get initial observation + return self._make_observation() + + def step(self, action: DoomAction) -> DoomObservation: + """ + Execute agent's action and return resulting observation. + + Args: + action: DoomAction containing either action_id or buttons. + + Returns: + Observation after action execution. + + Raises: + ValueError: If action is invalid. + """ + if not isinstance(action, DoomAction): + raise ValueError(f"Expected DoomAction, got {type(action)}") + + # Convert action to button presses + if action.action_id is not None: + # Use discrete action + if not self.use_discrete_actions or self.discrete_actions is None: + raise ValueError("discrete actions not enabled") + if action.action_id < 0 or action.action_id >= len(self.discrete_actions): + raise ValueError( + f"Invalid action_id: {action.action_id}. " + f"Valid range: [0, {len(self.discrete_actions) - 1}]" + ) + buttons = self.discrete_actions[action.action_id] + elif action.buttons is not None: + # Use button combination + if len(action.buttons) != self.num_buttons: + raise ValueError( + f"Invalid button count: {len(action.buttons)}. " + f"Expected {self.num_buttons} buttons." + ) + buttons = action.buttons + else: + raise ValueError("Either action_id or buttons must be provided") + + # Execute action + reward = self.game.make_action(buttons) + + self._state.step_count += 1 + + # Get observation + obs = self._make_observation() + obs.reward = float(reward) + + return obs + + @property + def state(self) -> State: + """Get current environment state.""" + return self._state + + def _make_observation(self) -> DoomObservation: + """ + Create a DoomObservation from current game state. + + Returns: + DoomObservation for the agent. + """ + # Check if episode is finished + episode_finished = self.game.is_episode_finished() + + if episode_finished: + # Return empty observation when episode is done + screen_flat = [0] * int(np.prod(self.screen_shape)) + game_vars = [] + else: + # Get current state + state = self.game.get_state() + + # Get screen buffer + if state.screen_buffer is not None: + screen = state.screen_buffer + # Flatten screen for JSON serialization + screen_flat = screen.flatten().tolist() + self.screen_shape = list(screen.shape) + else: + screen_flat = [0] * int(np.prod(self.screen_shape)) + + # Get game variables + if hasattr(state, "game_variables"): + game_vars = ( + state.game_variables.tolist() + if state.game_variables is not None + else [] + ) + else: + game_vars = [] + + # Get available actions + if self.use_discrete_actions and self.discrete_actions is not None: + available_actions = list(range(len(self.discrete_actions))) + else: + available_actions = [] + + # Create observation + obs = DoomObservation( + screen_buffer=screen_flat, + screen_shape=self.screen_shape, + game_variables=game_vars, + available_actions=available_actions, + episode_finished=episode_finished, + done=episode_finished, + reward=0.0, # Will be filled in by step() + metadata={ + "scenario": self.scenario, + "num_buttons": self.num_buttons, + "available_buttons": [str(b) for b in self.available_buttons], + }, + ) + + return obs + + def render(self, mode: str = "human") -> Optional[np.ndarray]: + """ + Render the environment. + + Args: + mode: Render mode - "human" for window display, "rgb_array" for array return. + + Returns: + RGB array if mode is "rgb_array", None otherwise. + """ + if self.game.is_episode_finished(): + # Can't render if episode is finished + if mode == "rgb_array": + return np.zeros(self.screen_shape, dtype=np.uint8) + return None + + state = self.game.get_state() + if state is None or state.screen_buffer is None: + if mode == "rgb_array": + return np.zeros(self.screen_shape, dtype=np.uint8) + return None + + screen = state.screen_buffer + + if mode == "rgb_array": + # Return the screen buffer as numpy array + return screen + elif mode == "human": + # Display using matplotlib or cv2 + try: + import cv2 + + # Create window if it doesn't exist + if self._render_window is None: + self._render_window = "ViZDoom - Doom Environment" + cv2.namedWindow(self._render_window, cv2.WINDOW_NORMAL) + + # Convert to BGR for OpenCV (if RGB) + if len(screen.shape) == 3 and screen.shape[2] == 3: + screen_bgr = cv2.cvtColor(screen, cv2.COLOR_RGB2BGR) + else: + screen_bgr = screen + + # Display + cv2.imshow(self._render_window, screen_bgr) + cv2.waitKey(1) # Small delay to update window + + except ImportError: + # Fallback to matplotlib if cv2 not available + try: + import matplotlib.pyplot as plt + + if self._render_window is None: + plt.ion() # Enable interactive mode + self._render_window = plt.figure(figsize=(8, 6)) + self._render_window.canvas.manager.set_window_title( + "ViZDoom - Doom Environment" + ) + + plt.clf() + if len(screen.shape) == 3: + plt.imshow(screen) + else: + plt.imshow(screen, cmap="gray") + plt.axis("off") + plt.pause(0.001) # Small delay to update window + + except ImportError: + print( + "Warning: Neither cv2 nor matplotlib available for rendering. " + "Install with: pip install opencv-python or pip install matplotlib" + ) + return None + else: + raise ValueError( + f"Invalid render mode: {mode}. Use 'human' or 'rgb_array'." + ) + + def close(self) -> None: + """Clean up resources.""" + # Close render window if it exists + if self._render_window is not None: + try: + import cv2 + + cv2.destroyAllWindows() + except ImportError: + try: + import matplotlib.pyplot as plt + + plt.close("all") + except ImportError: + # If matplotlib is not installed, we cannot close its windows, but this is non-critical during cleanup. + pass + self._render_window = None + + # Close the game + if hasattr(self, "game") and self.game is not None: + self.game.close() + + def __del__(self): + """Destructor to ensure game is closed.""" + self.close() diff --git a/src/envs/doom_env/server/requirements.txt b/src/envs/doom_env/server/requirements.txt new file mode 100644 index 000000000..7f38351a8 --- /dev/null +++ b/src/envs/doom_env/server/requirements.txt @@ -0,0 +1,3 @@ +# ViZDoom and dependencies +vizdoom>=1.2.0 +numpy>=1.19.0 diff --git a/src/envs/doom_env/tests/README.md b/src/envs/doom_env/tests/README.md new file mode 100644 index 000000000..d6226a411 --- /dev/null +++ b/src/envs/doom_env/tests/README.md @@ -0,0 +1,255 @@ +# Doom Environment Tests + +Comprehensive test suite for the Doom environment implementation. + +## Test Structure + +``` +tests/ +├── __init__.py # Test package initialization +├── conftest.py # Shared fixtures and pytest configuration +├── test_models.py # Unit tests for DoomAction and DoomObservation (15 tests) +├── test_doom_environment.py # Unit tests for DoomEnvironment (18 tests) +├── test_doom_client.py # Unit tests for DoomEnv HTTP client (20 tests) +└── test_doom_integration.py # Integration tests (12 tests) +``` + +## Running Tests + +### Quick Start + +```bash +# Run all fast unit tests (no server required) +pytest tests/test_models.py tests/test_doom_client.py -v + +# Run environment tests (requires ViZDoom) +pytest tests/test_doom_environment.py -v + +# Run integration tests (requires server) +pytest tests/test_doom_integration.py -v + +# Run all tests +pytest tests/ -v +``` + +### Test Categories + +**Unit Tests (Fast)** +- `test_models.py` - Data model validation (15 tests) +- `test_doom_client.py` - HTTP client tests (20 tests) + +**Environment Tests (Requires ViZDoom)** +- `test_doom_environment.py` - ViZDoom wrapper tests (18 tests) + +**Integration Tests (Requires Server)** +- `test_doom_integration.py` - End-to-end tests (12 tests) + +### Using Test Markers + +```bash +# Run only fast tests (skip slow integration tests) +pytest tests/ -m "not slow" -v + +# Run only tests that require server +pytest tests/ -m "requires_server" -v + +# Run only tests that don't require ViZDoom +pytest tests/ -m "not requires_vizdoom" -v + +# Skip Docker tests +pytest tests/ -m "not requires_docker" -v +``` + +### Test Coverage + +```bash +# Run with coverage report (requires pytest-cov) +pytest tests/ --cov=. --cov-report=html --cov-report=term + +# View coverage report +open htmlcov/index.html +``` + +### Continuous Integration + +For CI/CD pipelines: + +```bash +# Run only unit tests (no server/ViZDoom required) +pytest tests/test_models.py tests/test_doom_client.py -v + +# Run with minimal output for CI logs +pytest tests/ -v --tb=short --disable-warnings +``` + +## Test Requirements + +### Minimal (Unit Tests) +```bash +pip install pytest numpy +``` + +### Full (All Tests) +```bash +pip install -e . # Installs doom_env with all dependencies +pip install pytest pytest-cov +``` + +## Test Fixtures + +### Available Fixtures + +- `sample_observation` - Pre-configured DoomObservation +- `sample_action` - Pre-configured DoomAction +- `sample_screen_buffer_160x120` - Screen buffer for 160x120 resolution +- `sample_screen_buffer_640x480` - Screen buffer for 640x480 resolution +- `doom_server` - Running Doom server (for integration tests) + +### Usage Example + +```python +def test_my_feature(sample_observation): + # Use the fixture + assert sample_observation.screen_shape == [10, 10, 3] +``` + +## Writing New Tests + +### Test Naming Convention + +- Test files: `test_*.py` +- Test classes: `Test*` +- Test functions: `test_*` + +### Example Test + +```python +import pytest +from ..models import DoomAction + +class TestMyFeature: + def test_basic_functionality(self): + """Test description.""" + action = DoomAction(action_id=0) + assert action.action_id == 0 + + @pytest.mark.slow + def test_slow_operation(self): + """Mark slow tests.""" + # Long-running test + pass + + @pytest.mark.requires_vizdoom + def test_vizdoom_feature(self): + """Mark tests requiring ViZDoom.""" + from ..server.doom_env_environment import DoomEnvironment + env = DoomEnvironment() + # Test code +``` + +## Troubleshooting + +### ImportError: ViZDoom not found +```bash +# Install ViZDoom system dependencies (Ubuntu/Debian) +sudo apt-get install build-essential cmake libboost-all-dev libsdl2-dev + +# Install ViZDoom Python package +pip install vizdoom +``` + +### Server connection refused +```bash +# Start the server in another terminal +python -m doom_env.server.app + +# Or run integration tests with server fixture (starts automatically) +pytest tests/test_doom_integration.py -v +``` + +### Tests fail in headless environment +```bash +# Set environment variables for headless mode +DOOM_WINDOW_VISIBLE=false pytest tests/ -v + +# Skip display-dependent tests +pytest tests/ -m "not requires_display" -v +``` + +## Test Metrics + +**Target Coverage**: >80% + +**Performance Goals**: +- Unit tests: < 10 seconds total +- Environment tests: < 30 seconds total +- Integration tests: < 2 minutes total + +## Test Details + +### Model Tests (test_models.py) + +Tests data models without external dependencies: +- DoomAction creation, validation, serialization +- DoomObservation structure, fields, edge cases +- Numpy type conversion +- Screen buffer/shape validation +- Large buffers (800x600 RGB) + +### Environment Tests (test_doom_environment.py) + +Tests ViZDoom wrapper (requires ViZDoom installed): +- Environment initialization with various configs +- Reset/step functionality +- Screen buffer formats (RGB24, GRAY8) +- Multiple resolutions (160x120, 320x240, 640x480) +- Game variables tracking +- Episode termination +- Multiple scenarios support + +### Client Tests (test_doom_client.py) + +Tests HTTP client (uses mocks, no server needed): +- Client initialization +- Step payload serialization +- Numpy int64 conversion +- Response parsing +- Rendering modes +- Resource cleanup + +### Integration Tests (test_doom_integration.py) + +Tests full system (requires running server): +- Complete episode playthrough +- Client-server communication +- Reset after episode completion +- Reward accumulation +- Health tracking +- Multiple resets +- Performance benchmarks + +## Contributing + +When adding new features: + +1. Write tests first (TDD) +2. Ensure all tests pass +3. Add appropriate markers (`@pytest.mark.slow`, etc.) +4. Update this README if adding new test categories +5. Maintain >80% code coverage + +## CI/CD Integration + +Example GitHub Actions workflow: + +```yaml +- name: Run unit tests + run: | + pip install pytest numpy + pytest tests/test_models.py tests/test_doom_client.py -v + +- name: Run with coverage + run: | + pip install pytest-cov + pytest tests/ -m "not slow" --cov=. --cov-report=xml +``` diff --git a/src/envs/doom_env/tests/__init__.py b/src/envs/doom_env/tests/__init__.py new file mode 100644 index 000000000..b3b076933 --- /dev/null +++ b/src/envs/doom_env/tests/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +"""Test suite for Doom environment.""" diff --git a/src/envs/doom_env/tests/conftest.py b/src/envs/doom_env/tests/conftest.py new file mode 100644 index 000000000..db4e36823 --- /dev/null +++ b/src/envs/doom_env/tests/conftest.py @@ -0,0 +1,69 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Pytest configuration and shared fixtures for Doom environment tests. +""" + +import pytest + + +def pytest_configure(config): + """Register custom markers.""" + config.addinivalue_line( + "markers", "slow: marks tests as slow (deselect with '-m \"not slow\"')" + ) + config.addinivalue_line( + "markers", "requires_server: marks tests that require a running server" + ) + config.addinivalue_line( + "markers", "requires_vizdoom: marks tests that require ViZDoom installed" + ) + config.addinivalue_line( + "markers", "requires_docker: marks tests that require Docker" + ) + config.addinivalue_line( + "markers", "requires_display: marks tests that require display/X11" + ) + + +@pytest.fixture +def sample_observation(): + """Create a sample DoomObservation for testing.""" + from ..models import DoomObservation + + return DoomObservation( + screen_buffer=[128] * 300, # 10x10x3 + screen_shape=[10, 10, 3], + game_variables=[100.0, 50.0], + available_actions=[0, 1, 2, 3], + episode_finished=False, + done=False, + reward=0.0, + metadata={"episode": 1} + ) + + +@pytest.fixture +def sample_action(): + """Create a sample DoomAction for testing.""" + from ..models import DoomAction + + return DoomAction(action_id=0) + + +@pytest.fixture +def sample_screen_buffer_160x120(): + """Create a sample screen buffer for 160x120 resolution.""" + # RGB24: 120 height x 160 width x 3 channels + return [128] * (120 * 160 * 3) + + +@pytest.fixture +def sample_screen_buffer_640x480(): + """Create a sample screen buffer for 640x480 resolution.""" + # RGB24: 480 height x 640 width x 3 channels + return [255] * (480 * 640 * 3) diff --git a/src/envs/doom_env/tests/test_doom_client.py b/src/envs/doom_env/tests/test_doom_client.py new file mode 100644 index 000000000..c778b9123 --- /dev/null +++ b/src/envs/doom_env/tests/test_doom_client.py @@ -0,0 +1,370 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Test suite for DoomEnv HTTP client. + +Tests client-server communication, serialization, and rendering. +""" + +import pytest +import numpy as np +from unittest.mock import Mock, patch, MagicMock + +from ..client import DoomEnv +from ..models import DoomAction, DoomObservation +from openenv_core.client_types import StepResult +from openenv_core.env_server.types import State + + +class TestDoomClient: + """Tests for DoomEnv HTTP client.""" + + def test_client_initialization(self): + """Verify DoomEnv client can be created with base_url.""" + client = DoomEnv(base_url="http://localhost:8000") + + assert client is not None + # DoomEnv inherits from HTTPEnvClient which stores URL internally + # We verify initialization succeeded by checking internal state + assert hasattr(client, '_last_observation') + assert hasattr(client, '_render_window') + + def test_client_initialization_invalid_url(self): + """Test client creation with invalid URL format.""" + # Should still create client (connection tested on first request) + client = DoomEnv(base_url="not-a-valid-url") + assert client is not None + + def test_step_payload_serialization(self): + """Ensure _step_payload() converts DoomAction to JSON correctly.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Test with regular Python int + action1 = DoomAction(action_id=5) + payload1 = client._step_payload(action1) + + assert isinstance(payload1, dict) + assert "action_id" in payload1 + assert payload1["action_id"] == 5 + assert isinstance(payload1["action_id"], int) + + def test_step_payload_numpy_int64_conversion(self): + """Test _step_payload() converts numpy int64 to native Python int.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Test with numpy int64 (common from np.random.choice) + np_action_id = np.int64(7) + action = DoomAction(action_id=int(np_action_id)) + payload = client._step_payload(action) + + assert isinstance(payload["action_id"], int) + assert payload["action_id"] == 7 + + def test_step_payload_with_buttons(self): + """Test _step_payload() handles button list correctly.""" + client = DoomEnv(base_url="http://localhost:8000") + + buttons = [True, False, True, False] + action = DoomAction(action_id=0, buttons=buttons) + payload = client._step_payload(action) + + assert "action_id" in payload + assert "buttons" in payload + assert payload["buttons"] == buttons + + def test_step_payload_filters_none_values(self): + """Test _step_payload() filters out None values.""" + client = DoomEnv(base_url="http://localhost:8000") + + action = DoomAction(action_id=3, buttons=None) + payload = client._step_payload(action) + + # None values should be filtered out + assert "action_id" in payload + assert "buttons" not in payload # Should be filtered since it's None + + def test_parse_result_structure(self): + """Verify _parse_result() converts JSON to DoomObservation.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Mock server response + server_response = { + "observation": { + "screen_buffer": [255, 128, 0] * 100, + "screen_shape": [10, 10, 3], + "game_variables": [100.0, 50.0], + "available_actions": [0, 1, 2, 3], + "episode_finished": False, + "done": False, + "metadata": {"episode": 1} + }, + "reward": 5.0, + "done": False + } + + result = client._parse_result(server_response) + + assert isinstance(result, StepResult) + assert isinstance(result.observation, DoomObservation) + assert result.reward == 5.0 + assert result.done is False + + def test_parse_result_observation_fields(self): + """Test _parse_result() correctly parses all observation fields.""" + client = DoomEnv(base_url="http://localhost:8000") + + server_response = { + "observation": { + "screen_buffer": [128] * 300, + "screen_shape": [10, 10, 3], + "game_variables": [75.0], + "available_actions": [0, 1, 2], + "episode_finished": False, + "done": False, + "metadata": {"scenario": "basic"} + }, + "reward": 10.0, + "done": False + } + + result = client._parse_result(server_response) + obs = result.observation + + assert obs.screen_buffer == [128] * 300 + assert obs.screen_shape == [10, 10, 3] + assert obs.game_variables == [75.0] + assert obs.available_actions == [0, 1, 2] + assert obs.episode_finished is False + assert obs.metadata == {"scenario": "basic"} + + def test_parse_result_with_missing_fields(self): + """Test _parse_result() handles missing optional fields.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Minimal response + server_response = { + "observation": { + "screen_buffer": [], + "screen_shape": [120, 160, 3], + "episode_finished": False, + "done": True, + "metadata": {} + }, + "reward": 0.0, + "done": True + } + + result = client._parse_result(server_response) + + assert isinstance(result.observation, DoomObservation) + assert result.observation.game_variables is None # Should default to None + assert result.done is True + + def test_parse_state(self): + """Verify _parse_state() converts JSON to State object.""" + client = DoomEnv(base_url="http://localhost:8000") + + state_response = { + "episode_id": 5, + "step_count": 42 + } + + state = client._parse_state(state_response) + + assert isinstance(state, State) + assert state.episode_id == 5 + assert state.step_count == 42 + + def test_parse_state_with_missing_step_count(self): + """Test _parse_state() handles missing step_count.""" + client = DoomEnv(base_url="http://localhost:8000") + + state_response = { + "episode_id": 3 + } + + state = client._parse_state(state_response) + + assert state.episode_id == 3 + assert state.step_count == 0 # Should default to 0 + + def test_render_rgb_array_mode(self): + """Verify render(mode='rgb_array') returns numpy array.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Mock last observation with correct buffer size (10 * 10 * 3 = 300) + client._last_observation = DoomObservation( + screen_buffer=[255, 128, 0] * 100, # 300 elements total + screen_shape=[10, 10, 3], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + + result = client.render(mode="rgb_array") + + assert isinstance(result, np.ndarray) + assert result.shape == (10, 10, 3) + assert result.dtype == np.uint8 + + def test_render_rgb_array_pixel_values(self): + """Test render() produces correct pixel values.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Create specific pattern + screen_buffer = [255, 0, 0, 0, 255, 0] # Red pixel, Green pixel + client._last_observation = DoomObservation( + screen_buffer=screen_buffer, + screen_shape=[1, 2, 3], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + + result = client.render(mode="rgb_array") + + assert result[0, 0, 0] == 255 # Red channel + assert result[0, 0, 1] == 0 # Green channel + assert result[0, 0, 2] == 0 # Blue channel + assert result[0, 1, 0] == 0 # Second pixel red + assert result[0, 1, 1] == 255 # Second pixel green + + def test_render_without_observation(self): + """Test render() when no observation is available.""" + client = DoomEnv(base_url="http://localhost:8000") + + # No observation set + result = client.render(mode="rgb_array") + + assert result is None + + def test_render_invalid_mode(self): + """Test render() with invalid mode raises error.""" + client = DoomEnv(base_url="http://localhost:8000") + + client._last_observation = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + + with pytest.raises(ValueError, match="Invalid render mode"): + client.render(mode="invalid_mode") + + @patch('cv2.namedWindow') + @patch('cv2.imshow') + @patch('cv2.waitKey', return_value=ord('q')) + @patch('cv2.cvtColor', return_value=np.zeros((10, 10, 3), dtype=np.uint8)) + def test_render_human_mode_with_cv2(self, mock_cvtColor, mock_waitKey, mock_imshow, mock_namedWindow): + """Test render(mode='human') uses OpenCV when available.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Mock last observation with correct buffer size + client._last_observation = DoomObservation( + screen_buffer=[128] * 300, # 10 * 10 * 3 = 300 + screen_shape=[10, 10, 3], + game_variables=[100.0], + available_actions=[0, 1, 2, 3], + episode_finished=False, + done=False, + reward=5.0, + metadata={} + ) + + result = client.render(mode="human") + + # Should return None in human mode + assert result is None + + # Should have called cv2 functions + mock_namedWindow.assert_called_once() + mock_imshow.assert_called_once() + + def test_close_cleanup(self): + """Verify client.close() cleans up resources.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Set up some state + client._last_observation = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + + # Close should not raise error + client.close() + + # Window should be cleared + assert client._render_window is None + + @patch('cv2.destroyAllWindows') + def test_close_destroys_cv2_windows(self, mock_destroyAllWindows): + """Test close() destroys OpenCV windows if they exist.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Simulate window creation + client._render_window = "test_window" + + client.close() + + # Should have called destroyAllWindows + mock_destroyAllWindows.assert_called_once() + assert client._render_window is None + + def test_step_payload_with_numpy_array_buttons(self): + """Test _step_payload() handles numpy arrays in buttons.""" + client = DoomEnv(base_url="http://localhost:8000") + + # Buttons as numpy array + buttons = np.array([True, False, True]) + action = DoomAction(action_id=0, buttons=buttons.tolist()) + payload = client._step_payload(action) + + assert "buttons" in payload + assert isinstance(payload["buttons"], list) + assert payload["buttons"] == [True, False, True] + + def test_observation_stored_for_rendering(self): + """Test that _parse_result() stores observation for rendering.""" + client = DoomEnv(base_url="http://localhost:8000") + + server_response = { + "observation": { + "screen_buffer": [255] * 100, + "screen_shape": [10, 10, 1], + "game_variables": None, + "available_actions": None, + "episode_finished": False, + "done": False, + "metadata": {} + }, + "reward": 0.0, + "done": False + } + + result = client._parse_result(server_response) + + # Observation should be stored in _last_observation + assert client._last_observation is not None + assert client._last_observation == result.observation + assert client._last_observation.screen_buffer == [255] * 100 diff --git a/src/envs/doom_env/tests/test_doom_environment.py b/src/envs/doom_env/tests/test_doom_environment.py new file mode 100644 index 000000000..48b22826b --- /dev/null +++ b/src/envs/doom_env/tests/test_doom_environment.py @@ -0,0 +1,359 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Test suite for DoomEnvironment server implementation. + +Tests the core ViZDoom wrapper and environment logic. +""" + +import pytest + +try: + from ..server.doom_env_environment import DoomEnvironment + from ..models import DoomAction, DoomObservation + VIZDOOM_AVAILABLE = True +except ImportError: + VIZDOOM_AVAILABLE = False + pytestmark = pytest.mark.skip("ViZDoom not installed") + + +@pytest.mark.skipif(not VIZDOOM_AVAILABLE, reason="ViZDoom not installed") +class TestDoomEnvironment: + """Tests for DoomEnvironment class.""" + + def test_environment_initialization(self): + """Verify DoomEnvironment initializes with correct configuration.""" + env = DoomEnvironment( + scenario="basic", + screen_resolution="RES_160X120", + screen_format="RGB24", + window_visible=False, + use_discrete_actions=True + ) + + assert env is not None + assert env.scenario == "basic" + assert env.screen_resolution == "RES_160X120" + + def test_environment_initialization_invalid_scenario(self): + """Test initialization with invalid scenario name.""" + with pytest.raises(Exception): # ViZDoom will raise an error + env = DoomEnvironment( + scenario="nonexistent_scenario", + screen_resolution="RES_160X120", + screen_format="RGB24", + window_visible=False + ) + + def test_environment_reset(self): + """Verify reset() returns valid initial observation.""" + env = DoomEnvironment( + scenario="basic", + screen_resolution="RES_160X120", + window_visible=False + ) + + obs = env.reset() + + assert isinstance(obs, DoomObservation) + assert obs.screen_buffer is not None + assert obs.screen_shape is not None + assert obs.done is False + assert obs.episode_finished is False + + def test_environment_step_with_valid_action(self): + """Verify step() processes actions correctly.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + env.reset() + + action = DoomAction(action_id=0) # No-op + obs = env.step(action) + + assert isinstance(obs, DoomObservation) + assert obs.screen_buffer is not None + assert isinstance(obs.reward, float) + assert isinstance(obs.done, bool) + + def test_environment_step_returns_correct_structure(self): + """Ensure step() returns properly structured DoomObservation.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + env.reset() + + action = DoomAction(action_id=1) # Move left + obs = env.step(action) + + # Verify all required fields are present + assert hasattr(obs, 'screen_buffer') + assert hasattr(obs, 'screen_shape') + assert hasattr(obs, 'game_variables') + assert hasattr(obs, 'available_actions') + assert hasattr(obs, 'episode_finished') + assert hasattr(obs, 'done') + assert hasattr(obs, 'reward') + assert hasattr(obs, 'metadata') + + def test_screen_buffer_format(self): + """Verify screen buffer is flattened RGB data.""" + env = DoomEnvironment( + scenario="basic", + screen_resolution="RES_160X120", + screen_format="RGB24", + window_visible=False + ) + obs = env.reset() + + # Screen buffer should be a flat list + assert isinstance(obs.screen_buffer, list) + + # For RGB24, each pixel has 3 values + expected_size = 120 * 160 * 3 # H x W x C + assert len(obs.screen_buffer) == expected_size + + # All values should be in valid pixel range [0, 255] + assert all(0 <= pixel <= 255 for pixel in obs.screen_buffer) + + def test_screen_shape_consistency(self): + """Ensure screen_shape matches configured resolution.""" + test_cases = [ + ("RES_160X120", [120, 160, 3]), + ("RES_320X240", [240, 320, 3]), + ("RES_640X480", [480, 640, 3]), + ] + + for resolution, expected_shape in test_cases: + env = DoomEnvironment( + scenario="basic", + screen_resolution=resolution, + screen_format="RGB24", + window_visible=False + ) + obs = env.reset() + + assert obs.screen_shape == expected_shape, ( + f"Resolution {resolution} should produce shape {expected_shape}, " + f"got {obs.screen_shape}" + ) + + def test_game_variables_health(self): + """Verify game_variables includes health value.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + obs = env.reset() + + assert obs.game_variables is not None + assert len(obs.game_variables) > 0 + + # First variable should be health + health = obs.game_variables[0] + assert isinstance(health, float) + assert 0 <= health <= 100 # Health should be in valid range + + def test_episode_termination(self): + """Verify done flag is set when episode ends.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + env.reset() + + # Step until episode ends (with timeout) + max_steps = 1000 + done = False + + for _ in range(max_steps): + action = DoomAction(action_id=3) # Attack + obs = env.step(action) + + if obs.done or obs.episode_finished: + done = True + break + + # Episode should eventually end + # Note: In some scenarios it might not end within max_steps + # We test that the flags exist and are boolean + assert isinstance(obs.done, bool) + assert isinstance(obs.episode_finished, bool) + + def test_reward_calculation(self): + """Ensure rewards are calculated correctly.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + env.reset() + + # Take some actions + rewards = [] + for _ in range(10): + action = DoomAction(action_id=2) # Move right + obs = env.step(action) + rewards.append(obs.reward) + + # Verify rewards are numeric + assert all(isinstance(r, (int, float)) for r in rewards) + + # At least some rewards should be non-None + assert rewards is not None + + def test_available_actions(self): + """Verify available_actions list is correct for scenario.""" + env = DoomEnvironment( + scenario="basic", + window_visible=False, + use_discrete_actions=True + ) + obs = env.reset() + + assert obs.available_actions is not None + assert isinstance(obs.available_actions, list) + + # Basic scenario should have discrete actions + assert len(obs.available_actions) > 0 + + # Actions should be integers + assert all(isinstance(a, int) for a in obs.available_actions) + + def test_discrete_actions_vs_button_combinations(self): + """Verify use_discrete_actions flag works correctly.""" + # Test discrete actions mode + env_discrete = DoomEnvironment( + scenario="basic", + window_visible=False, + use_discrete_actions=True + ) + obs_discrete = env_discrete.reset() + + # Should have discrete actions + assert obs_discrete.available_actions is not None + assert all(isinstance(a, int) for a in obs_discrete.available_actions) + + # Test button combinations mode + env_buttons = DoomEnvironment( + scenario="basic", + window_visible=False, + use_discrete_actions=False + ) + obs_buttons = env_buttons.reset() + + # Both should produce valid observations + assert isinstance(obs_discrete, DoomObservation) + assert isinstance(obs_buttons, DoomObservation) + + def test_multiple_scenarios(self): + """Verify different scenarios load correctly.""" + scenarios = ["basic", "deadly_corridor", "defend_the_center"] + + for scenario in scenarios: + try: + env = DoomEnvironment( + scenario=scenario, + window_visible=False + ) + obs = env.reset() + + assert isinstance(obs, DoomObservation) + assert obs.metadata.get("scenario") == scenario + except Exception as e: + # Some scenarios might not be available + pytest.skip(f"Scenario {scenario} not available: {e}") + + def test_window_visible_flag(self): + """Verify window_visible parameter controls rendering window.""" + # Headless mode (should always work) + env_headless = DoomEnvironment( + scenario="basic", + window_visible=False + ) + obs = env_headless.reset() + assert obs is not None + + # Window mode might fail in headless environments + # We just verify the parameter is accepted + try: + env_window = DoomEnvironment( + scenario="basic", + window_visible=True + ) + obs_window = env_window.reset() + assert obs_window is not None + except Exception: + # Expected in headless/Docker environments + pytest.skip("Display not available for window mode") + + def test_state_consistency(self): + """Ensure environment state remains consistent across steps.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + + # First episode + obs1 = env.reset() + episode_id_1 = obs1.metadata.get("episode_id", 0) + + # Take some steps + for _ in range(5): + action = DoomAction(action_id=0) + env.step(action) + + # Reset and start new episode + obs2 = env.reset() + episode_id_2 = obs2.metadata.get("episode_id", 0) + + # Episode ID should increment or reset properly + assert episode_id_2 >= episode_id_1 + + def test_multiple_resets(self): + """Verify multiple reset() calls work correctly.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + + # Reset multiple times + for i in range(5): + obs = env.reset() + + assert isinstance(obs, DoomObservation) + assert obs.done is False + assert obs.episode_finished is False + + # Screen should not be empty + assert len(obs.screen_buffer) > 0 + + def test_step_after_done(self): + """Test that stepping after episode completion works.""" + env = DoomEnvironment(scenario="basic", window_visible=False) + env.reset() + + # Force episode to end (attack until done or max steps) + max_steps = 500 + for _ in range(max_steps): + action = DoomAction(action_id=3) # Attack + obs = env.step(action) + + if obs.done: + break + + # If episode ended, try to step again + if obs.done: + # Should handle gracefully (either continue or require reset) + action = DoomAction(action_id=0) + try: + obs_after = env.step(action) + # Either returns final state or new state + assert isinstance(obs_after, DoomObservation) + except Exception: + # Some implementations require reset after done + pytest.skip("Environment requires reset after done") + + def test_grayscale_format(self): + """Test environment with grayscale screen format.""" + env = DoomEnvironment( + scenario="basic", + screen_resolution="RES_160X120", + screen_format="GRAY8", + window_visible=False + ) + obs = env.reset() + + # Grayscale returns 2D shape [height, width], not 3D [height, width, 1] + # This is standard behavior for grayscale images in ViZDoom + assert len(obs.screen_shape) == 2 # 2D array for grayscale + assert obs.screen_shape[0] == 120 # height + assert obs.screen_shape[1] == 160 # width + + # Buffer size should match H * W + expected_size = obs.screen_shape[0] * obs.screen_shape[1] + assert len(obs.screen_buffer) == expected_size diff --git a/src/envs/doom_env/tests/test_doom_integration.py b/src/envs/doom_env/tests/test_doom_integration.py new file mode 100644 index 000000000..07ba17c29 --- /dev/null +++ b/src/envs/doom_env/tests/test_doom_integration.py @@ -0,0 +1,364 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Integration tests for Doom environment. + +End-to-end tests that require a running server. +These tests are marked as 'slow' and 'requires_server'. +""" + +import pytest +import time +import subprocess +import requests +import numpy as np + +from ..client import DoomEnv +from ..models import DoomAction, DoomObservation + + +@pytest.fixture(scope="module") +def doom_server(): + """Start Doom server for integration tests.""" + # Start server process + server_process = subprocess.Popen( + ["python", "-m", "doom_env.server.app"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + + # Wait for server to start + max_retries = 10 + for _ in range(max_retries): + try: + response = requests.get("http://localhost:8000/health") + if response.status_code == 200: + break + except requests.ConnectionError: + time.sleep(1) + else: + server_process.kill() + pytest.fail("Server failed to start") + + yield "http://localhost:8000" + + # Cleanup + server_process.terminate() + server_process.wait(timeout=5) + + +@pytest.mark.slow +@pytest.mark.requires_server +class TestDoomIntegration: + """Integration tests for full Doom environment.""" + + def test_client_server_connection(self, doom_server): + """Test that client can connect to server.""" + client = DoomEnv(base_url=doom_server) + + # Should be able to get health status + response = requests.get(f"{doom_server}/health") + assert response.status_code == 200 + + def test_full_episode_playthrough(self, doom_server): + """Run a complete episode from reset to done.""" + client = DoomEnv(base_url=doom_server) + + # Reset environment + result = client.reset() + + assert result.observation is not None + assert isinstance(result.observation, DoomObservation) + assert result.observation.done is False + + # Take some actions + total_reward = 0.0 + steps = 0 + max_steps = 100 + + for _ in range(max_steps): + # Choose random action + available_actions = result.observation.available_actions + if available_actions: + action_id = int(np.random.choice(available_actions)) + else: + action_id = 0 + + action = DoomAction(action_id=action_id) + result = client.step(action) + + total_reward += result.reward + steps += 1 + + if result.observation.done: + break + + # Verify we took at least one step + assert steps > 0 + + # Verify reward is numeric + assert isinstance(total_reward, (int, float)) + + client.close() + + def test_reset_after_episode_completion(self, doom_server): + """Verify reset works after episode completes.""" + client = DoomEnv(base_url=doom_server) + + # First episode + result1 = client.reset() + initial_health_1 = result1.observation.game_variables[0] if result1.observation.game_variables else None + + # Take actions until done or max steps + max_steps = 500 + for _ in range(max_steps): + action = DoomAction(action_id=3) # Attack + result = client.step(action) + + if result.observation.done: + break + + # Reset and start new episode + result2 = client.reset() + + assert result2.observation.done is False + assert result2.observation.episode_finished is False + + # Health should be reset + if result2.observation.game_variables: + initial_health_2 = result2.observation.game_variables[0] + # Health should be back to full or at least higher than before + assert initial_health_2 >= 0 + + client.close() + + def test_reward_accumulation(self, doom_server): + """Verify rewards accumulate correctly over episode.""" + client = DoomEnv(base_url=doom_server) + + client.reset() + + rewards = [] + for _ in range(20): + action = DoomAction(action_id=int(np.random.randint(0, 4))) + result = client.step(action) + rewards.append(result.reward) + + if result.observation.done: + break + + # All rewards should be numeric + assert all(isinstance(r, (int, float)) for r in rewards) + + # Calculate total reward + total_reward = sum(rewards) + assert isinstance(total_reward, (int, float)) + + client.close() + + def test_health_tracking(self, doom_server): + """Verify health tracking through game variables.""" + client = DoomEnv(base_url=doom_server) + + result = client.reset() + + health_values = [] + + # Track health over several steps + for _ in range(30): + if result.observation.game_variables: + health = result.observation.game_variables[0] + health_values.append(health) + + action = DoomAction(action_id=0) # No-op + result = client.step(action) + + if result.observation.done: + break + + # Health should be tracked + assert len(health_values) > 0 + + # All health values should be valid + assert all(isinstance(h, (int, float)) for h in health_values) + assert all(0 <= h <= 100 for h in health_values) + + client.close() + + def test_action_effects(self, doom_server): + """Verify different actions have different effects.""" + client = DoomEnv(base_url=doom_server) + + client.reset() + + observations = [] + + # Test different actions + test_actions = [0, 1, 2, 3] # No-op, Left, Right, Attack + + for action_id in test_actions: + action = DoomAction(action_id=action_id) + result = client.step(action) + observations.append(result.observation) + + # All observations should be valid + assert all(isinstance(obs, DoomObservation) for obs in observations) + + # Screen buffers should be present + assert all(len(obs.screen_buffer) > 0 for obs in observations) + + client.close() + + def test_multiple_resets(self, doom_server): + """Verify multiple reset() calls work correctly.""" + client = DoomEnv(base_url=doom_server) + + episode_ids = [] + + for _ in range(5): + result = client.reset() + + assert result.observation.done is False + assert result.observation.episode_finished is False + + # Track episode ID if available + if result.observation.metadata: + episode_id = result.observation.metadata.get("episode_id") + if episode_id is not None: + episode_ids.append(episode_id) + + # Take a few steps + for _ in range(5): + action = DoomAction(action_id=0) + client.step(action) + + # Should have completed multiple resets + assert len(episode_ids) <= 5 + + client.close() + + def test_state_endpoint(self, doom_server): + """Test that state endpoint returns correct information.""" + client = DoomEnv(base_url=doom_server) + + client.reset() + + # Get initial state + state1 = client.state() + + assert state1.episode_id is not None + assert state1.step_count == 0 + + # Take some steps + for _ in range(10): + action = DoomAction(action_id=0) + client.step(action) + + # Get state after steps + state2 = client.state() + + # Step count should have increased + assert state2.step_count > state1.step_count + + client.close() + + def test_screen_buffer_consistency(self, doom_server): + """Verify screen buffer size is consistent with shape.""" + client = DoomEnv(base_url=doom_server) + + result = client.reset() + obs = result.observation + + # Calculate expected buffer size + height, width, channels = obs.screen_shape + expected_size = height * width * channels + + # Buffer size should match shape + assert len(obs.screen_buffer) == expected_size + + # Take a step and verify again + action = DoomAction(action_id=0) + result = client.step(action) + obs = result.observation + + height, width, channels = obs.screen_shape + expected_size = height * width * channels + assert len(obs.screen_buffer) == expected_size + + client.close() + + def test_observation_serialization_round_trip(self, doom_server): + """Test that observations survive serialization round-trip.""" + client = DoomEnv(base_url=doom_server) + + result = client.reset() + original_obs = result.observation + + # Observation should have been serialized over HTTP and deserialized + assert isinstance(original_obs, DoomObservation) + assert isinstance(original_obs.screen_buffer, list) + assert isinstance(original_obs.screen_shape, list) + + # Take a step + action = DoomAction(action_id=1) + result = client.step(action) + step_obs = result.observation + + # Verify structure is consistent + assert len(step_obs.screen_shape) == len(original_obs.screen_shape) + + client.close() + + +@pytest.mark.slow +class TestPerformance: + """Performance and stress tests.""" + + def test_rapid_steps(self, doom_server): + """Test rapid consecutive steps.""" + client = DoomEnv(base_url=doom_server) + + client.reset() + + start_time = time.time() + num_steps = 100 + + for _ in range(num_steps): + action = DoomAction(action_id=0) + result = client.step(action) + + if result.observation.done: + client.reset() + + elapsed_time = time.time() - start_time + + # Should complete in reasonable time (< 10 seconds for 100 steps) + assert elapsed_time < 10.0 + + # Calculate steps per second + steps_per_second = num_steps / elapsed_time + assert steps_per_second > 5 # At least 5 steps per second + + client.close() + + def test_memory_leak_detection(self, doom_server): + """Test for memory leaks with repeated resets.""" + client = DoomEnv(base_url=doom_server) + + # Perform many resets + for _ in range(20): + client.reset() + + # Take a few steps + for _ in range(10): + action = DoomAction(action_id=0) + client.step(action) + + # If we got here without crashing, no obvious memory leak + assert True + + client.close() diff --git a/src/envs/doom_env/tests/test_models.py b/src/envs/doom_env/tests/test_models.py new file mode 100644 index 000000000..52d49c245 --- /dev/null +++ b/src/envs/doom_env/tests/test_models.py @@ -0,0 +1,300 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +""" +Test suite for Doom models (DoomAction and DoomObservation). + +Tests data model validation, serialization, and edge cases. +""" + +import numpy as np +from dataclasses import asdict + +from ..models import DoomAction, DoomObservation + + +class TestDoomAction: + """Tests for DoomAction model.""" + + def test_doom_action_creation(self): + """Verify that DoomAction can be created with valid action_id.""" + action = DoomAction(action_id=0) + assert action.action_id == 0 + + action = DoomAction(action_id=3) + assert action.action_id == 3 + + def test_doom_action_with_negative_id(self): + """Test DoomAction with negative action_id (edge case).""" + # Negative action_id should be allowed (let ViZDoom validate) + action = DoomAction(action_id=-1) + assert action.action_id == -1 + + def test_doom_action_with_buttons(self): + """Test DoomAction with custom button list.""" + buttons = [True, False, True] + action = DoomAction(action_id=0, buttons=buttons) + assert action.action_id == 0 + assert action.buttons == buttons + + def test_doom_action_serialization(self): + """Verify DoomAction can be serialized to dict.""" + action = DoomAction(action_id=2) + action_dict = asdict(action) + + assert isinstance(action_dict, dict) + assert "action_id" in action_dict + assert action_dict["action_id"] == 2 + + def test_doom_action_numpy_int64_conversion(self): + """Test DoomAction with numpy int64 (common edge case).""" + # This is important for when actions come from np.random.choice + np_action_id = np.int64(5) + action = DoomAction(action_id=int(np_action_id)) + assert isinstance(action.action_id, int) + assert action.action_id == 5 + + def test_doom_action_with_none_buttons(self): + """Test DoomAction with None buttons (default).""" + action = DoomAction(action_id=1, buttons=None) + assert action.action_id == 1 + assert action.buttons is None + + +class TestDoomObservation: + """Tests for DoomObservation model.""" + + def test_doom_observation_structure(self): + """Verify DoomObservation has all required fields.""" + obs = DoomObservation( + screen_buffer=[0, 255, 128], + screen_shape=[120, 160, 3], + game_variables=[100.0], + available_actions=[0, 1, 2, 3], + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + + assert obs.screen_buffer == [0, 255, 128] + assert obs.screen_shape == [120, 160, 3] + assert obs.game_variables == [100.0] + assert obs.available_actions == [0, 1, 2, 3] + assert obs.episode_finished is False + assert obs.done is False + assert obs.reward == 0.0 + assert obs.metadata == {} + + def test_doom_observation_screen_buffer_validation(self): + """Ensure screen_buffer is a list of integers.""" + # Valid screen buffer + obs = DoomObservation( + screen_buffer=[0, 128, 255] * 100, + screen_shape=[10, 10, 3], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + assert isinstance(obs.screen_buffer, list) + assert all(isinstance(x, int) for x in obs.screen_buffer) + + def test_doom_observation_screen_shape_validation(self): + """Ensure screen_shape matches expected format [H, W, C].""" + # Test different resolutions + resolutions = [ + [120, 160, 3], # RES_160X120 + [240, 320, 3], # RES_320X240 + [480, 640, 3], # RES_640X480 + [120, 160, 1], # Grayscale + ] + + for shape in resolutions: + buffer_size = shape[0] * shape[1] * shape[2] + obs = DoomObservation( + screen_buffer=[0] * buffer_size, + screen_shape=shape, + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + assert obs.screen_shape == shape + assert len(obs.screen_shape) == 3 + + def test_doom_observation_game_variables(self): + """Verify game_variables field holds ViZDoom state info.""" + # With health only + obs = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=[100.0], + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + assert obs.game_variables == [100.0] + + # With None (no variables available) + obs2 = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + assert obs2.game_variables is None + + # With multiple variables (health, ammo, etc.) + obs3 = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=[100.0, 50.0, 26.0], + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + assert len(obs3.game_variables) == 3 + + def test_doom_observation_serialization(self): + """Verify DoomObservation can serialize/deserialize correctly.""" + original_obs = DoomObservation( + screen_buffer=[255, 128, 0] * 10, + screen_shape=[5, 2, 3], + game_variables=[100.0, 50.0], + available_actions=[0, 1, 2, 3], + episode_finished=False, + done=False, + reward=5.0, + metadata={"episode": 1} + ) + + # Serialize to dict + obs_dict = asdict(original_obs) + + # Verify structure + assert isinstance(obs_dict, dict) + assert obs_dict["screen_buffer"] == original_obs.screen_buffer + assert obs_dict["screen_shape"] == original_obs.screen_shape + assert obs_dict["game_variables"] == original_obs.game_variables + assert obs_dict["reward"] == 5.0 + + # Deserialize back + reconstructed_obs = DoomObservation(**obs_dict) + assert reconstructed_obs.screen_buffer == original_obs.screen_buffer + assert reconstructed_obs.screen_shape == original_obs.screen_shape + assert reconstructed_obs.reward == original_obs.reward + + def test_doom_observation_empty_screen_buffer(self): + """Test observation with empty screen buffer (edge case).""" + obs = DoomObservation( + screen_buffer=[], + screen_shape=[0, 0, 0], + game_variables=None, + available_actions=None, + episode_finished=True, + done=True, + reward=0.0, + metadata={} + ) + assert obs.screen_buffer == [] + assert obs.done is True + + def test_doom_observation_large_screen_buffer(self): + """Test observation with large screen buffer (high resolution).""" + # 800x600 RGB + height, width, channels = 600, 800, 3 + buffer_size = height * width * channels + + obs = DoomObservation( + screen_buffer=[128] * buffer_size, + screen_shape=[height, width, channels], + game_variables=[100.0], + available_actions=[0, 1, 2, 3], + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + + assert len(obs.screen_buffer) == buffer_size + assert obs.screen_shape == [height, width, channels] + + def test_doom_observation_reward_types(self): + """Test observation with different reward values.""" + # Positive reward (kill) + obs1 = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=100.0, + metadata={} + ) + assert obs1.reward == 100.0 + + # Negative reward (damage) + obs2 = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=-10.0, + metadata={} + ) + assert obs2.reward == -10.0 + + # Zero reward (no event) + obs3 = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata={} + ) + assert obs3.reward == 0.0 + + def test_doom_observation_metadata(self): + """Test observation metadata field.""" + metadata = { + "episode": 5, + "total_steps": 1000, + "scenario": "basic" + } + + obs = DoomObservation( + screen_buffer=[0], + screen_shape=[1, 1, 1], + game_variables=None, + available_actions=None, + episode_finished=False, + done=False, + reward=0.0, + metadata=metadata + ) + + assert obs.metadata == metadata + assert obs.metadata["episode"] == 5 + assert obs.metadata["scenario"] == "basic" diff --git a/src/envs/doom_env/uv.lock b/src/envs/doom_env/uv.lock new file mode 100644 index 000000000..acafd2de8 --- /dev/null +++ b/src/envs/doom_env/uv.lock @@ -0,0 +1,1864 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version < '3.11' and 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