Add support for gymnasium v1.0

.github/workflows/ci.yml

@@ -20,7 +20,12 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.8", "3.9", "3.10", "3.11"]
-
+        include:
+          # Default version
+          - gymnasium-version: "1.0.0"
+          # Add a new config to test gym<1.0
+          - python-version: "3.10"
+            gymnasium-version: "0.29.1"
     steps:
       - uses: actions/checkout@v3
         with:
@@ -32,22 +37,22 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-
           # Use uv for faster downloads
           pip install uv
-          # Install Atari Roms
-          uv pip install --system autorom
-          wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
-          base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
-          AutoROM --accept-license --source-file Roms.tar.gz
-
+          # cpu version of pytorch
           # See https://github.com/astral-sh/uv/issues/1497
           uv pip install --system torch==2.4.1+cpu --index https://download.pytorch.org/whl/cpu
           # Install full requirements (for additional envs and test tools)
           uv pip install --system -r requirements.txt
           # Use headless version
           uv pip install --system opencv-python-headless
           uv pip install --system -e .[plots,tests]
+
+      - name: Install specific version of gym
+        run: |
+          uv pip install --system gymnasium==${{ matrix.gymnasium-version }}
+        # Only run for python 3.10, downgrade gym to 0.29.1
+
       - name: Lint with ruff
         run: |
           make lint

.github/workflows/trained_agents.yml

@@ -21,7 +21,12 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.8", "3.9", "3.10", "3.11"]
-
+        include:
+          # Default version
+          - gymnasium-version: "1.0.0"
+          # Add a new config to test gym<1.0
+          - python-version: "3.10"
+            gymnasium-version: "0.29.1"
     steps:
       - uses: actions/checkout@v3
         with:
@@ -36,19 +41,21 @@ jobs:
 
           # Use uv for faster downloads
           pip install uv
-          # Install Atari Roms
-          uv pip install --system autorom
-          wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
-          base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
-          AutoROM --accept-license --source-file Roms.tar.gz
-
+          # cpu version of pytorch
           # See https://github.com/astral-sh/uv/issues/1497
           uv pip install --system torch==2.4.1+cpu --index https://download.pytorch.org/whl/cpu
           # Install full requirements (for additional envs and test tools)
+          # Install full requirements (for additional envs and test tools)
           uv pip install --system -r requirements.txt
           # Use headless version
           uv pip install --system opencv-python-headless
           uv pip install --system -e .[plots,tests]
+
+      - name: Install specific version of gym
+        run: |
+          uv pip install --system gymnasium==${{ matrix.gymnasium-version }}
+        # Only run for python 3.10, downgrade gym to 0.29.1
+
       - name: Check trained agents
         run: |
           make check-trained-agents

CHANGELOG.md

@@ -1,13 +1,16 @@
-## Release 2.4.0a10 (WIP)
+## Release 2.4.0a11 (WIP)
 
-**New algorithm: CrossQ, and better defaults for SAC/TQC on Swimmer-v4 env**
+**New algorithm: CrossQ, Gymnasium v1.0 support, and better defaults for SAC/TQC on Swimmer-v4 env**
 
 ### Breaking Changes
 - Updated defaults hyperparameters for TQC/SAC for Swimmer-v4 (decrease gamma for more consistent results) (@JacobHA) [W&B report](https://wandb.ai/openrlbenchmark/sbx/reports/SAC-MuJoCo-Swimmer-v4--Vmlldzo3NzM5OTk2)
 - Upgraded to SB3 >= 2.4.0
+- Renamed `LunarLander-v2` to `LunarLander-v3` in hyperparameters
 
 ### New Features
 - Added `CrossQ` hyperparameters for SB3-contrib (@danielpalen)
+- Added Gymnasium v1.0 support
+- `--custom-objects` in `enjoy.py` now also patches obs space (when bounds are changed) to solve "Observation spaces do not match" errors
 
 ### Bug fixes
 - Replaced deprecated `huggingface_hub.Repository` when pushing to Hugging Face Hub by the recommended `HfApi` (see https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http) (@cochaviz)

hyperparams/a2c.yml

@@ -61,7 +61,7 @@ Pendulum-v1:
   policy_kwargs: "dict(log_std_init=-2, ortho_init=False)"
 
 # Tuned
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   normalize: true
   n_envs: 4
   n_timesteps: !!float 5e6

hyperparams/ars.yml

@@ -26,7 +26,7 @@ LunarLander-v2:
   n_timesteps: !!float 2e6
 
 # Tuned
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   <<: *pendulum-params
   n_timesteps: !!float 2e6
 
@@ -215,4 +215,3 @@ A1Jumping-v0:
   # alive_bonus_offset: -1
   normalize: "dict(norm_obs=True, norm_reward=False)"
   # policy_kwargs: "dict(net_arch=[16])"
-

hyperparams/crossq.yml

@@ -18,7 +18,7 @@ Pendulum-v1:
   policy_kwargs: "dict(net_arch=[256, 256])"
 
 
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   n_timesteps: !!float 2e5
   policy: 'MlpPolicy'
   buffer_size: 1000000

hyperparams/ddpg.yml

@@ -23,7 +23,7 @@ Pendulum-v1:
   learning_rate: !!float 1e-3
   policy_kwargs: "dict(net_arch=[400, 300])"
 
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   n_timesteps: !!float 3e5
   policy: 'MlpPolicy'
   gamma: 0.98

hyperparams/ppo.yml

@@ -122,7 +122,7 @@ LunarLander-v2:
   n_epochs: 4
   ent_coef: 0.01
 
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   n_envs: 16
   n_timesteps: !!float 1e6
   policy: 'MlpPolicy'

hyperparams/sac.yml

@@ -22,7 +22,7 @@ Pendulum-v1:
   learning_rate: !!float 1e-3
 
 
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   n_timesteps: !!float 5e5
   policy: 'MlpPolicy'
   batch_size: 256

hyperparams/td3.yml

@@ -23,7 +23,7 @@ Pendulum-v1:
   learning_rate: !!float 1e-3
   policy_kwargs: "dict(net_arch=[400, 300])"
 
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   n_timesteps: !!float 3e5
   policy: 'MlpPolicy'
   gamma: 0.98

hyperparams/tqc.yml

@@ -19,7 +19,7 @@ Pendulum-v1:
   policy: 'MlpPolicy'
   learning_rate: !!float 1e-3
 
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   n_timesteps: !!float 5e5
   policy: 'MlpPolicy'
   learning_rate: lin_7.3e-4

hyperparams/trpo.yml

@@ -35,7 +35,7 @@ LunarLander-v2:
   n_critic_updates: 15
 
 # Tuned
-LunarLanderContinuous-v2:
+LunarLanderContinuous-v3:
   normalize: true
   n_envs: 2
   n_timesteps: !!float 1e5

requirements.txt

@@ -1,12 +1,12 @@
 gym==0.26.2
-stable-baselines3[extra_no_roms,tests,docs]>=2.4.0a10,<3.0
+stable-baselines3[extra,tests,docs]>=2.4.0a11,<3.0
 box2d-py==2.3.8
-pybullet_envs_gymnasium>=0.4.0
+pybullet_envs_gymnasium>=0.5.0
 # minigrid
 cloudpickle>=2.2.1
 # optuna plots:
 plotly
 # need to upgrade to gymnasium:
 # panda-gym~=3.0.1
 wandb
-moviepy
+moviepy>=1.0.0

rl_zoo3/enjoy.py

@@ -184,12 +184,23 @@ def enjoy() -> None:  # noqa: C901
             "learning_rate": 0.0,
             "lr_schedule": lambda _: 0.0,
             "clip_range": lambda _: 0.0,
+            # load models with different obs bounds
+            # Note: doesn't work with channel last envs
+            # "observation_space": env.observation_space,
         }
 
     if "HerReplayBuffer" in hyperparams.get("replay_buffer_class", ""):
         kwargs["env"] = env
 
     model = ALGOS[algo].load(model_path, custom_objects=custom_objects, device=args.device, **kwargs)
+    # Uncomment to save patched file (for instance gym -> gymnasium)
+    # model.save(model_path)
+    # Patch VecNormalize (gym -> gymnasium)
+    # from pathlib import Path
+    # env.observation_space = model.observation_space
+    # env.action_space = model.action_space
+    # env.save(Path(model_path).parent / env_name / "vecnormalize.pkl")
+
     obs = env.reset()
 
     # Deterministic by default except for atari games

rl_zoo3/gym_patches.py

@@ -39,5 +39,8 @@ def step(self, action):
 
 # Patch Gymnasium TimeLimit
 gymnasium.wrappers.TimeLimit = PatchedTimeLimit  # type: ignore[misc]
-gymnasium.wrappers.time_limit.TimeLimit = PatchedTimeLimit  # type: ignore[misc]
+try:
+    gymnasium.wrappers.time_limit.TimeLimit = PatchedTimeLimit  # type: ignore[misc]
+except AttributeError:
+    gymnasium.wrappers.common.TimeLimit = PatchedTimeLimit  # type: ignore
 gymnasium.envs.registration.TimeLimit = PatchedTimeLimit  # type: ignore[misc,attr-defined]

rl_zoo3/import_envs.py

@@ -1,7 +1,7 @@
 from typing import Callable, Optional
 
 import gymnasium as gym
-from gymnasium.envs.registration import register
+from gymnasium.envs.registration import register, register_envs
 
 from rl_zoo3.wrappers import MaskVelocityWrapper
 
@@ -10,6 +10,14 @@
 except ImportError:
     pass
 
+try:
+    import ale_py
+
+    # no-op
+    gym.register_envs(ale_py)
+except ImportError:
+    pass
+
 try:
     import highway_env
 except ImportError:

rl_zoo3/version.txt

@@ -1 +1 @@
-2.4.0a10
+2.4.0a11

setup.py

@@ -15,21 +15,22 @@
 See https://github.com/DLR-RM/rl-baselines3-zoo
 """
 install_requires = [
-    "sb3_contrib>=2.4.0a10,<3.0",
-    "gymnasium~=0.29.1",
+    "sb3_contrib>=2.4.0a11,<3.0",
+    "gymnasium>=0.29.1,<1.1.0",
     "huggingface_sb3>=3.0,<4.0",
     "tqdm",
     "rich",
     "optuna>=3.0",
     "pyyaml>=5.1",
     "pytablewriter~=1.2",
+    "shimmy~=2.0",
 ]
 plots_requires = ["seaborn", "rliable~=1.2.0", "scipy~=1.10"]
 test_requires = [
     # for MuJoCo envs v4:
-    "mujoco~=2.3",
+    "mujoco>=2.3,<4",
     # install parking-env to test HER
-    "highway-env==1.8.2",
+    "highway-env>=1.10.1,<1.11.0",
 ]
 
 setup(

tests/test_enjoy.py

@@ -1,6 +1,7 @@
 import os
 import shlex
 import subprocess
+from importlib.metadata import version
 
 import pytest
 
@@ -40,6 +41,11 @@ def test_trained_agents(trained_model):
     if "Panda" in env_id:
         return
 
+    # TODO: rename trained agents once we drop support for gymnasium v0.29
+    if "Lander" in env_id and version("gymnasium") > "0.29.1":
+        # LunarLander-v2 is now LunarLander-v3
+        return
+
     # Skip mujoco envs
     if "Fetch" in trained_model or "-v3" in trained_model:
         return

tests/test_train.py

@@ -1,6 +1,7 @@
 import os
 import shlex
 import subprocess
+from importlib.metadata import version
 
 import pytest
 
@@ -36,10 +37,17 @@ def test_train(tmp_path, experiment):
 
 
 def test_continue_training(tmp_path):
-    algo, env_id = "a2c", "CartPole-v1"
+    algo = "a2c"
+    if version("gymnasium") > "0.29.1":
+        # See https://github.com/DLR-RM/stable-baselines3/pull/1837#issuecomment-2457322341
+        # obs bounds have changed...
+        env_id = "CartPole-v1"
+    else:
+        env_id = "Pendulum-v1"
+
     cmd = (
         f"python train.py -n {N_STEPS} --algo {algo} --env {env_id} --log-folder {tmp_path} "
-        "-i rl-trained-agents/a2c/CartPole-v1_1/CartPole-v1.zip"
+        f"-i rl-trained-agents/a2c/{env_id}_1/{env_id}.zip"
     )
     return_code = subprocess.call(shlex.split(cmd))
     _assert_eq(return_code, 0)