Add TD3 main executable file

README.md

@@ -102,7 +102,11 @@ For example (DDPG):
 
   ![AC](AC/A2CAgent_600.gif)
 
-- improve `AWR`, `DDPG` with Gumbel Distribution Regression from [`XQL`](https://div99.github.io/XQL):
+- [x] [TD3](https://arxiv.org/pdf/1802.09477.pdf)
+
+  ![TD3](TD3/TD3Agent_100.gif)
+
+- improve `AWR`, `DDPG` `TD3` with Gumbel Distribution Regression from [`XQL`](https://div99.github.io/XQL):
   - XAWR
 
     ![XAWR](XAWR/XAWRAgent_100.gif)
@@ -111,6 +115,10 @@ For example (DDPG):
 
     ![XDDPG](XDDPG/XDDPGAgent_200.gif)
 
+  - XTD3
+
+    ![XTD3](XTD3/XTD3Agent_100.gif)
+
 ## Reference
 
 - TrainMonitor and Generategif modified from [coax](https://github.com/coax-dev/coax)

TD3/__init__.py

@@ -0,0 +1 @@
+# pylint: disable=all

TD3/main.py

@@ -0,0 +1,136 @@
+"""main executable file for TD3"""
+import os
+import logging
+from itertools import repeat
+import gymnasium as gym
+import torch
+import numpy as np
+from util import generate_gif
+from util.wrappers import TrainMonitor
+from util.buffer import Experience
+from collections import deque
+# pylint: disable=invalid-name
+from TD3.td3 import TD3Agent as TD3_torch
+
+Agent = TD3_torch
+logging.basicConfig(level=logging.INFO)
+
+torch.manual_seed(0)
+np.random.seed(0)
+
+EPSILON_DECAY_STEPS = 100
+
+
+def main(
+    n_episodes=2000,
+    max_t=200,
+    eps_start=1.0,
+    eps_end=0.01,
+    eps_decay=0.996,
+    score_term_rules=lambda s: False,
+    time_interval="25ms"
+):
+  # pylint: disable=line-too-long
+  """Deep Q-Learning
+
+    Params
+    ======
+        n_episodes (int): maximum number of training epsiodes
+        max_t (int): maximum number of timesteps per episode
+        eps_start (float): starting value of epsilon, for epsilon-greedy action selection
+        eps_end (float): minimum value of epsilon
+        eps_decay (float): mutiplicative factor (per episode) for decreasing epsilon
+
+    """
+  scores = []  # list containing score from each episode
+  scores_window = deque(maxlen=100)  # last 100 scores
+  eps = eps_start
+
+  env = gym.make(
+      "Pendulum-v1",
+      render_mode="rgb_array",
+  )
+  # env = gym.make(
+  #     "LunarLander-v2",
+  #     render_mode="rgb_array",
+  #     continuous=True,
+  # )
+  # env = gym.make("MountainCarContinuous-v0", render_mode="rgb_array")
+  env = TrainMonitor(env, tensorboard_dir="./logs", tensorboard_write_all=True)
+
+  gamma = 0.99
+  batch_size = 64
+  learn_iteration = 16
+  update_tau = 0.5
+
+  lr_actor = 0.0001
+  lr_critic = 0.001
+
+  mu = 0.0
+  theta = 0.15
+  max_sigma = 0.3
+  min_sigma = 0.3
+  decay_period = 100000
+  value_noise_clip = 0.5
+  value_noise_sigma = 0.5
+
+  agent = Agent(
+      state_dims=env.observation_space,
+      action_space=env.action_space,
+      lr_actor=lr_actor,
+      lr_critic=lr_critic,
+      gamma=gamma,
+      batch_size=batch_size,
+      forget_experience=False,
+      update_tau=update_tau,
+      mu=mu,
+      theta=theta,
+      max_sigma=max_sigma,
+      min_sigma=min_sigma,
+      decay_period=decay_period,
+      value_noise_clip=value_noise_clip,
+      value_noise_sigma=value_noise_sigma
+  )
+  dump_gif_dir = f"images/{agent.__class__.__name__}/{agent.__class__.__name__}_{{}}.gif"
+  for i_episode in range(1, n_episodes + 1):
+    state, _ = env.reset()
+    score = 0
+    for t, _ in enumerate(repeat(0, max_t)):
+      action = agent.take_action(state=state, explore=True, step=t * i_episode)
+      next_state, reward, done, _, _ = env.step(action)
+      agent.remember(Experience(state, action, reward, next_state, done))
+      agent.learn(learn_iteration)
+
+      state = next_state
+      score += reward
+
+      if done or score_term_rules(score):
+        break
+
+      scores_window.append(score)  ## save the most recent score
+      scores.append(score)  ## sae the most recent score
+      eps = max(eps * eps_decay, eps_end)  ## decrease the epsilon
+      print(" " * os.get_terminal_size().columns, end="\r")
+      print(
+          f"\rEpisode {i_episode}\tAverage Score {np.mean(scores_window):.2f}",
+          end="\r"
+      )
+
+    if i_episode and i_episode % 100 == 0:
+      print(" " * os.get_terminal_size().columns, end="\r")
+      print(
+          f"\rEpisode {i_episode}\tAverage Score {np.mean(scores_window):.2f}"
+      )
+      generate_gif(
+          env,
+          filepath=dump_gif_dir.format(i_episode),
+          policy=lambda s: agent.take_action(s, explore=False),
+          duration=float(time_interval.split("ms")[0]),
+          max_episode_steps=max_t
+      )
+
+  return scores
+
+
+if __name__ == "__main__":
+  main()