Add gymnasium support for DQN

.github/workflows/tests.yaml

@@ -39,6 +39,10 @@ jobs:
       - name: Install jax
         if: runner.os == 'Linux' || runner.os == 'macOS'
         run: poetry install -E "pytest jax"
+      - name: Run gymnasium migration dependencies
+        run: poetry run pip install "stable_baselines3==2.0.0a1"
+      - name: Run gymnasium tests
+        run: poetry run pytest tests/test_classic_control_gymnasium.py
       - name: Run core tests with jax
         if: runner.os == 'Linux' || runner.os == 'macOS'
         run: poetry run pytest tests/test_classic_control_jax.py
@@ -78,6 +82,10 @@ jobs:
       - name: Install jax
         if: runner.os == 'Linux' || runner.os == 'macOS'
         run: poetry install -E "pytest atari jax"
+      - name: Run gymnasium migration dependencies
+        run: poetry run pip install "stable_baselines3==2.0.0a1" "gymnasium[atari,accept-rom-license]==0.28.1"  "ale-py==0.8.1" 
+      - name: Run gymnasium tests
+        run: poetry run pytest tests/test_atari_gymnasium.py
       - name: Run core tests with jax
         if: runner.os == 'Linux' || runner.os == 'macOS'
         run: poetry run pytest tests/test_atari_jax.py

cleanrl/dqn.py

@@ -5,7 +5,7 @@
 import time
 from distutils.util import strtobool
 
-import gym
+import gymnasium as gym
 import numpy as np
 import torch
 import torch.nn as nn
@@ -48,6 +48,8 @@ def parse_args():
         help="total timesteps of the experiments")
     parser.add_argument("--learning-rate", type=float, default=2.5e-4,
         help="the learning rate of the optimizer")
+    parser.add_argument("--num-envs", type=int, default=1,
+        help="the number of parallel game environments")
     parser.add_argument("--buffer-size", type=int, default=10000,
         help="the replay memory buffer size")
     parser.add_argument("--gamma", type=float, default=0.99,
@@ -70,19 +72,21 @@ def parse_args():
         help="the frequency of training")
     args = parser.parse_args()
     # fmt: on
+    assert args.num_envs == 1, "vectorized envs are not supported at the moment"
+
     return args
 
 
 def make_env(env_id, seed, idx, capture_video, run_name):
     def thunk():
-        env = gym.make(env_id)
+        if capture_video and idx == 0:
+            env = gym.make(env_id, render_mode="rgb_array")
+            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        else:
+            env = gym.make(env_id)
         env = gym.wrappers.RecordEpisodeStatistics(env)
-        if capture_video:
-            if idx == 0:
-                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
-        env.seed(seed)
         env.action_space.seed(seed)
-        env.observation_space.seed(seed)
+
         return env
 
     return thunk
@@ -110,6 +114,15 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
 
 
 if __name__ == "__main__":
+    import stable_baselines3 as sb3
+
+    if sb3.__version__ < "2.0":
+        raise ValueError(
+            """Ongoing migration: run the following command to install the new dependencies:
+
+poetry run pip install "stable_baselines3==2.0.0a1"
+"""
+        )
     args = parse_args()
     run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
     if args.track:
@@ -139,7 +152,9 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
 
     # env setup
-    envs = gym.vector.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
+    )
     assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     q_network = QNetwork(envs).to(device)
@@ -152,12 +167,12 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
         envs.single_observation_space,
         envs.single_action_space,
         device,
-        handle_timeout_termination=True,
+        handle_timeout_termination=False,
     )
     start_time = time.time()
 
     # TRY NOT TO MODIFY: start the game
-    obs = envs.reset()
+    obs, _ = envs.reset(seed=args.seed)
     for global_step in range(args.total_timesteps):
         # ALGO LOGIC: put action logic here
         epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
@@ -168,23 +183,25 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             actions = torch.argmax(q_values, dim=1).cpu().numpy()
 
         # TRY NOT TO MODIFY: execute the game and log data.
-        next_obs, rewards, dones, infos = envs.step(actions)
+        next_obs, rewards, terminated, truncated, infos = envs.step(actions)
 
         # TRY NOT TO MODIFY: record rewards for plotting purposes
-        for info in infos:
-            if "episode" in info.keys():
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                # Skip the envs that are not done
+                if "episode" not in info:
+                    continue
                 print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
                 writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
                 writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
                 writer.add_scalar("charts/epsilon", epsilon, global_step)
-                break
 
-        # TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
+        # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
         real_next_obs = next_obs.copy()
-        for idx, d in enumerate(dones):
+        for idx, d in enumerate(truncated):
             if d:
-                real_next_obs[idx] = infos[idx]["terminal_observation"]
-        rb.add(obs, real_next_obs, actions, rewards, dones, infos)
+                real_next_obs[idx] = infos["final_observation"][idx]
+        rb.add(obs, real_next_obs, actions, rewards, terminated, infos)
 
         # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
         obs = next_obs

cleanrl/dqn_atari.py

@@ -5,7 +5,7 @@
 import time
 from distutils.util import strtobool
 
-import gym
+import gymnasium as gym
 import numpy as np
 import torch
 import torch.nn as nn
@@ -55,6 +55,8 @@ def parse_args():
         help="total timesteps of the experiments")
     parser.add_argument("--learning-rate", type=float, default=1e-4,
         help="the learning rate of the optimizer")
+    parser.add_argument("--num-envs", type=int, default=1,
+        help="the number of parallel game environments")
     parser.add_argument("--buffer-size", type=int, default=1000000,
         help="the replay memory buffer size")
     parser.add_argument("--gamma", type=float, default=0.99,
@@ -77,16 +79,19 @@ def parse_args():
         help="the frequency of training")
     args = parser.parse_args()
     # fmt: on
+    assert args.num_envs == 1, "vectorized envs are not supported at the moment"
+
     return args
 
 
 def make_env(env_id, seed, idx, capture_video, run_name):
     def thunk():
-        env = gym.make(env_id)
+        if capture_video and idx == 0:
+            env = gym.make(env_id, render_mode="rgb_array")
+            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        else:
+            env = gym.make(env_id)
         env = gym.wrappers.RecordEpisodeStatistics(env)
-        if capture_video:
-            if idx == 0:
-                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
         env = NoopResetEnv(env, noop_max=30)
         env = MaxAndSkipEnv(env, skip=4)
         env = EpisodicLifeEnv(env)
@@ -96,9 +101,8 @@ def thunk():
         env = gym.wrappers.ResizeObservation(env, (84, 84))
         env = gym.wrappers.GrayScaleObservation(env)
         env = gym.wrappers.FrameStack(env, 4)
-        env.seed(seed)
         env.action_space.seed(seed)
-        env.observation_space.seed(seed)
+
         return env
 
     return thunk
@@ -131,6 +135,15 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
 
 
 if __name__ == "__main__":
+    import stable_baselines3 as sb3
+
+    if sb3.__version__ < "2.0":
+        raise ValueError(
+            """Ongoing migration: run the following command to install the new dependencies:
+
+poetry run pip install "stable_baselines3==2.0.0a1" "gymnasium[atari,accept-rom-license]==0.28.1"  "ale-py==0.8.1" 
+"""
+        )
     args = parse_args()
     run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
     if args.track:
@@ -160,7 +173,9 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
     device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
 
     # env setup
-    envs = gym.vector.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
+    )
     assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
     q_network = QNetwork(envs).to(device)
@@ -174,12 +189,12 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
         envs.single_action_space,
         device,
         optimize_memory_usage=True,
-        handle_timeout_termination=True,
+        handle_timeout_termination=False,
     )
     start_time = time.time()
 
     # TRY NOT TO MODIFY: start the game
-    obs = envs.reset()
+    obs, _ = envs.reset(seed=args.seed)
     for global_step in range(args.total_timesteps):
         # ALGO LOGIC: put action logic here
         epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
@@ -190,23 +205,25 @@ def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
             actions = torch.argmax(q_values, dim=1).cpu().numpy()
 
         # TRY NOT TO MODIFY: execute the game and log data.
-        next_obs, rewards, dones, infos = envs.step(actions)
+        next_obs, rewards, terminated, truncated, infos = envs.step(actions)
 
         # TRY NOT TO MODIFY: record rewards for plotting purposes
-        for info in infos:
-            if "episode" in info.keys():
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                # Skip the envs that are not done
+                if "episode" not in info:
+                    continue
                 print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
                 writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
                 writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
                 writer.add_scalar("charts/epsilon", epsilon, global_step)
-                break
 
-        # TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
+        # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
         real_next_obs = next_obs.copy()
-        for idx, d in enumerate(dones):
+        for idx, d in enumerate(truncated):
             if d:
-                real_next_obs[idx] = infos[idx]["terminal_observation"]
-        rb.add(obs, real_next_obs, actions, rewards, dones, infos)
+                real_next_obs[idx] = infos["final_observation"][idx]
+        rb.add(obs, real_next_obs, actions, rewards, terminated, infos)
 
         # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
         obs = next_obs