RGB image observation wrapper compatible with jit

.pre-commit-config.yaml

@@ -14,4 +14,4 @@ repos:
     rev: v1.1.350
     hooks:
       - id: pyright
-#        args: [--project=pyproject.toml]
+        args: [--project=pyproject.toml]

README.md

@@ -93,6 +93,7 @@ On the high level, current API combines [dm_env](https://github.com/google-deepm
 import jax
 import xminigrid
 from xminigrid.wrappers import GymAutoResetWrapper
+from xminigrid.experimental.img_obs import RGBImgObservationWrapper
 
 key = jax.random.PRNGKey(0)
 reset_key, ruleset_key = jax.random.split(key)
@@ -109,6 +110,9 @@ env_params = env_params.replace(ruleset=ruleset)
 # auto-reset wrapper
 env = GymAutoResetWrapper(env)
 
+# render obs as rgb images if needed (warn: this will affect speed greatly)
+env = RGBImgObservationWrapper(env)
+
 # fully jit-compatible step and reset methods
 timestep = jax.jit(env.reset)(env_params, reset_key)
 timestep = jax.jit(env.step)(env_params, timestep, action=0)

pyproject.toml

@@ -38,6 +38,8 @@ dependencies = [
     "flax>=0.8.0",
     "rich>=13.4.2",
     "chex>=0.1.85",
+    "imageio>=2.31.2",
+    "imageio-ffmpeg>=0.4.9",
 ]
 
 [project.optional-dependencies]
@@ -49,8 +51,6 @@ dev = [
 
 baselines = [
     "matplotlib>=3.7.2",
-    "imageio>=2.31.2",
-    "imageio-ffmpeg>=0.4.9",
     "wandb>=0.15.10",
     "pyrallis>=0.3.1",
     "distrax>=0.1.4",

scripts/benchmark_xland.py

@@ -15,15 +15,29 @@
 parser = argparse.ArgumentParser()
 parser.add_argument("--env-id", type=str, default="MiniGrid-Empty-16x16")
 parser.add_argument("--benchmark-id", type=str, default="Trivial")
+parser.add_argument("--img-obs", action="store_true")
 parser.add_argument("--timesteps", type=int, default=1000)
 parser.add_argument("--num-envs", type=int, default=8192)
 parser.add_argument("--num-repeat", type=int, default=10, help="Number of timing repeats")
 parser.add_argument("--num-iter", type=int, default=1, help="Number of runs during one repeat (time is summed)")
 
 
-def build_benchmark(env_id: str, num_envs: int, timesteps: int, benchmark_id: Optional[str] = None):
+def build_benchmark(
+    env_id: str,
+    num_envs: int,
+    timesteps: int,
+    benchmark_id: Optional[str] = None,
+    img_obs: bool = False,
+):
     env, env_params = xminigrid.make(env_id)
     env = GymAutoResetWrapper(env)
+
+    # enable img observations if needed
+    if img_obs:
+        from xminigrid.experimental.img_obs import RGBImgObservationWrapper
+
+        env = RGBImgObservationWrapper(env)
+
     # choose XLand benchmark if needed
     if "XLand-MiniGrid" in env_id and benchmark_id is not None:
         ruleset = load_benchmark(benchmark_id).sample_ruleset(jax.random.PRNGKey(0))
@@ -73,13 +87,15 @@ def timeit_benchmark(args, benchmark_fn):
     print("Num devices for pmap:", num_devices)
 
     # building for single env benchmarking
-    benchmark_fn_single = build_benchmark(args.env_id, 1, args.timesteps, args.benchmark_id)
+    benchmark_fn_single = build_benchmark(args.env_id, 1, args.timesteps, args.benchmark_id, args.img_obs)
     benchmark_fn_single = jax.jit(benchmark_fn_single)
     # building vmap for vectorization benchmarking
-    benchmark_fn_vmap = build_benchmark(args.env_id, args.num_envs, args.timesteps, args.benchmark_id)
+    benchmark_fn_vmap = build_benchmark(args.env_id, args.num_envs, args.timesteps, args.benchmark_id, args.img_obs)
     benchmark_fn_vmap = jax.jit(benchmark_fn_vmap)
     # building pmap for multi-gpu benchmarking (each doing (num_envs / num_devices) vmaps)
-    benchmark_fn_pmap = build_benchmark(args.env_id, args.num_envs // num_devices, args.timesteps, args.benchmark_id)
+    benchmark_fn_pmap = build_benchmark(
+        args.env_id, args.num_envs // num_devices, args.timesteps, args.benchmark_id, args.img_obs
+    )
     benchmark_fn_pmap = jax.pmap(benchmark_fn_pmap)
 
     key = jax.random.PRNGKey(0)

scripts/benchmark_xland_all.py

@@ -18,14 +18,24 @@
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--benchmark-id", type=str, default="trivial-1m")
+parser.add_argument("--img-obs", action="store_true")
 parser.add_argument("--timesteps", type=int, default=1000)
 parser.add_argument("--num-repeat", type=int, default=10, help="Number of timing repeats")
 parser.add_argument("--num-iter", type=int, default=1, help="Number of runs during one repeat (time is summed)")
 
 
-def build_benchmark(env_id: str, num_envs: int, timesteps: int, benchmark_id: Optional[str] = None):
+def build_benchmark(
+    env_id: str, num_envs: int, timesteps: int, benchmark_id: Optional[str] = None, img_obs: bool = False
+):
     env, env_params = xminigrid.make(env_id)
     env = GymAutoResetWrapper(env)
+
+    # enable img observations if needed
+    if img_obs:
+        from xminigrid.experimental.img_obs import RGBImgObservationWrapper
+
+        env = RGBImgObservationWrapper(env)
+
     # choose XLand benchmark if needed
     if "XLand-MiniGrid" in env_id and benchmark_id is not None:
         ruleset = load_benchmark(benchmark_id).sample_ruleset(jax.random.PRNGKey(0))
@@ -77,7 +87,9 @@ def timeit_benchmark(args, benchmark_fn):
         for env_id in tqdm(environments, desc="Envs.."):
             assert num_envs % num_devices == 0
             # building pmap for multi-gpu benchmarking (each doing (num_envs / num_devices) vmaps)
-            benchmark_fn_pmap = build_benchmark(env_id, num_envs // num_devices, args.timesteps, args.benchmark_id)
+            benchmark_fn_pmap = build_benchmark(
+                env_id, num_envs // num_devices, args.timesteps, args.benchmark_id, args.img_obs
+            )
             benchmark_fn_pmap = jax.pmap(benchmark_fn_pmap)
 
             # benchmarking

scripts/generate_benchmarks.sh

@@ -7,7 +7,6 @@ python scripts/ruleset_generator.py \
   --total_rulesets=1_000_000 \
   --save_path="trivial_1m"
 
-
 # small
 python scripts/ruleset_generator.py \
   --prune_chain \
@@ -41,17 +40,16 @@ python scripts/ruleset_generator.py \
   --total_rulesets=1_000_000 \
   --save_path="high_1m"
 
-
-# medium + distractors
-python scripts/ruleset_generator.py \
-  --prune_chain \
-  --prune_prob=0.8 \
-  --chain_depth=2 \
-  --sample_distractor_rules \
-  --num_distractor_rules=4 \
-  --num_distractor_objects=2 \
-  --total_rulesets=1_000_000 \
-  --save_path="medium_dist_1m"
+## medium + distractors
+#python scripts/ruleset_generator.py \
+#  --prune_chain \
+#  --prune_prob=0.8 \
+#  --chain_depth=2 \
+#  --sample_distractor_rules \
+#  --num_distractor_rules=4 \
+#  --num_distractor_objects=2 \
+#  --total_rulesets=1_000_000 \
+#  --save_path="medium_dist_1m"
 
 # medium 3M
 python scripts/ruleset_generator.py \

src/xminigrid/__init__.py

@@ -2,7 +2,7 @@
 from .registration import make, register, registered_environments
 
 # TODO: add __all__
-__version__ = "0.6.0"
+__version__ = "0.7.0"
 
 # ---------- XLand-MiniGrid environments ----------
 
@@ -210,7 +210,8 @@
 
 # BlockedUnlockPickUp
 register(
-    id="MiniGrid-BlockedUnlockPickUp", entry_point="xminigrid.envs.minigrid.blockedunlockpickup:BlockedUnlockPickUp"
+    id="MiniGrid-BlockedUnlockPickUp",
+    entry_point="xminigrid.envs.minigrid.blockedunlockpickup:BlockedUnlockPickUp",
 )
 
 # DoorKey

src/xminigrid/benchmarks.py

@@ -18,13 +18,12 @@
 DATA_PATH = os.environ.get("XLAND_MINIGRID_DATA", os.path.expanduser("~/.xland_minigrid"))
 
 NAME2HFFILENAME = {
-    "trivial-1m": "trivial_1m",
-    "small-1m": "small_1m",
-    "small-dist-1m": "small_dist_1m",
-    "medium-1m": "medium_1m_v1",
-    "medium-3m": "medium_3m_v1",
-    "high-1m": "high_1m",
-    "high-3m": "high_3m",
+    "trivial-1m": "trivial_1m_v2",
+    "small-1m": "small_1m_v2",
+    "medium-1m": "medium_1m_v2",
+    "medium-3m": "medium_3m_v2",
+    "high-1m": "high_1m_v2",
+    "high-3m": "high_3m_v2",
 }
 
 

src/xminigrid/core/constants.py

@@ -1,48 +1,44 @@
 import jax.numpy as jnp
 from flax import struct
 
+NUM_ACTIONS = 6
+
 # GRID: [tile, color]
 NUM_LAYERS = 2
-NUM_TILES = 15
-NUM_COLORS = 14
-NUM_ACTIONS = 6
+NUM_TILES = 13
+NUM_COLORS = 12
 
 
-# TODO: do we really need END_OF_MAP? seem like unseen can be used instead...
 # enums, kinda...
 class Tiles(struct.PyTreeNode):
     EMPTY: int = struct.field(pytree_node=False, default=0)
-    END_OF_MAP: int = struct.field(pytree_node=False, default=1)
-    UNSEEN: int = struct.field(pytree_node=False, default=2)
-    FLOOR: int = struct.field(pytree_node=False, default=3)
-    WALL: int = struct.field(pytree_node=False, default=4)
-    BALL: int = struct.field(pytree_node=False, default=5)
-    SQUARE: int = struct.field(pytree_node=False, default=6)
-    PYRAMID: int = struct.field(pytree_node=False, default=7)
-    GOAL: int = struct.field(pytree_node=False, default=8)
-    KEY: int = struct.field(pytree_node=False, default=9)
-    DOOR_LOCKED: int = struct.field(pytree_node=False, default=10)
-    DOOR_CLOSED: int = struct.field(pytree_node=False, default=11)
-    DOOR_OPEN: int = struct.field(pytree_node=False, default=12)
-    HEX: int = struct.field(pytree_node=False, default=13)
-    STAR: int = struct.field(pytree_node=False, default=14)
+    FLOOR: int = struct.field(pytree_node=False, default=1)
+    WALL: int = struct.field(pytree_node=False, default=2)
+    BALL: int = struct.field(pytree_node=False, default=3)
+    SQUARE: int = struct.field(pytree_node=False, default=4)
+    PYRAMID: int = struct.field(pytree_node=False, default=5)
+    GOAL: int = struct.field(pytree_node=False, default=6)
+    KEY: int = struct.field(pytree_node=False, default=7)
+    DOOR_LOCKED: int = struct.field(pytree_node=False, default=8)
+    DOOR_CLOSED: int = struct.field(pytree_node=False, default=9)
+    DOOR_OPEN: int = struct.field(pytree_node=False, default=10)
+    HEX: int = struct.field(pytree_node=False, default=11)
+    STAR: int = struct.field(pytree_node=False, default=12)
 
 
 class Colors(struct.PyTreeNode):
     EMPTY: int = struct.field(pytree_node=False, default=0)
-    END_OF_MAP: int = struct.field(pytree_node=False, default=1)
-    UNSEEN: int = struct.field(pytree_node=False, default=2)
-    RED: int = struct.field(pytree_node=False, default=3)
-    GREEN: int = struct.field(pytree_node=False, default=4)
-    BLUE: int = struct.field(pytree_node=False, default=5)
-    PURPLE: int = struct.field(pytree_node=False, default=6)
-    YELLOW: int = struct.field(pytree_node=False, default=7)
-    GREY: int = struct.field(pytree_node=False, default=8)
-    BLACK: int = struct.field(pytree_node=False, default=9)
-    ORANGE: int = struct.field(pytree_node=False, default=10)
-    WHITE: int = struct.field(pytree_node=False, default=11)
-    BROWN: int = struct.field(pytree_node=False, default=12)
-    PINK: int = struct.field(pytree_node=False, default=13)
+    RED: int = struct.field(pytree_node=False, default=1)
+    GREEN: int = struct.field(pytree_node=False, default=2)
+    BLUE: int = struct.field(pytree_node=False, default=3)
+    PURPLE: int = struct.field(pytree_node=False, default=4)
+    YELLOW: int = struct.field(pytree_node=False, default=5)
+    GREY: int = struct.field(pytree_node=False, default=6)
+    BLACK: int = struct.field(pytree_node=False, default=7)
+    ORANGE: int = struct.field(pytree_node=False, default=8)
+    WHITE: int = struct.field(pytree_node=False, default=9)
+    BROWN: int = struct.field(pytree_node=False, default=10)
+    PINK: int = struct.field(pytree_node=False, default=11)
 
 
 # Only ~100 combinations so far, better to preallocate them
@@ -65,7 +61,6 @@ class Colors(struct.PyTreeNode):
 
 WALKABLE = jnp.array(
     (
-        Tiles.EMPTY,
         Tiles.FLOOR,
         Tiles.GOAL,
         Tiles.DOOR_OPEN,
@@ -83,12 +78,7 @@ class Colors(struct.PyTreeNode):
     )
 )
 
-FREE_TO_PUT_DOWN = jnp.array(
-    (
-        Tiles.EMPTY,
-        Tiles.FLOOR,
-    )
-)
+FREE_TO_PUT_DOWN = jnp.array((Tiles.FLOOR,))
 
 LOS_BLOCKING = jnp.array(
     (

src/xminigrid/core/grid.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Callable, Union
+from typing import Callable
 
 import jax
 import jax.numpy as jnp
@@ -22,7 +22,7 @@ def equal(tile1: Tile, tile2: Tile) -> Tile:
 
 def get_neighbouring_tiles(grid: GridState, y: IntOrArray, x: IntOrArray) -> tuple[Tile, Tile, Tile, Tile]:
     # end_of_map = TILES_REGISTRY[Tiles.END_OF_MAP, Colors.END_OF_MAP]
-    end_of_map = Tiles.END_OF_MAP
+    end_of_map = Tiles.EMPTY
 
     up_tile = grid.at[y - 1, x].get(mode="fill", fill_value=end_of_map)
     right_tile = grid.at[y, x + 1].get(mode="fill", fill_value=end_of_map)

src/xminigrid/core/observation.py

@@ -12,7 +12,7 @@ def crop_field_of_view(grid: GridState, agent: AgentState, height: int, width: i
     grid = jnp.pad(
         grid,
         pad_width=((height, height), (width, width), (0, 0)),
-        constant_values=Tiles.END_OF_MAP,
+        constant_values=Tiles.EMPTY,
     )
     # account for padding
     y = agent.position[0] + height
@@ -110,8 +110,8 @@ def minigrid_field_of_view(grid: GridState, agent: AgentState, height: int, widt
     fov_grid = crop_field_of_view(grid, agent, height, width)
     fov_grid = align_with_up(fov_grid, agent.direction)
     mask = generate_viz_mask_minigrid(fov_grid)
-    # set UNSEEN value for all layers (including colors, as UNSEEN color has same id value)
-    fov_grid = jnp.where(mask[..., None], fov_grid, Tiles.UNSEEN)
+    # set EMPTY as unseen value for all layers (including colors, as EMPTY color has same id value)
+    fov_grid = jnp.where(mask[..., None], fov_grid, Tiles.EMPTY)
 
     # TODO: should we even do this? Agent with good memory can remember what he picked up.
     # WARN: this can overwrite tile the agent is on, GOAL for example.