Update PyBullet example

docs/guide/examples.rst

@@ -397,14 +397,14 @@ PyBullet: Normalizing input features
 ------------------------------------
 
 Normalizing input features may be essential to successful training of an RL agent
-(by default, images are scaled but not other types of input),
-for instance when training on `PyBullet <https://github.com/bulletphysics/bullet3/>`__ environments. For that, a wrapper exists and
-will compute a running average and standard deviation of input features (it can do the same for rewards).
+(by default, images are scaled, but other types of input are not),
+for instance when training on `PyBullet <https://github.com/bulletphysics/bullet3/>`__ environments.
+For this, there is a wrapper ``VecNormalize`` that will compute a running average and standard deviation of the input features (it can do the same for rewards).
 
 
 .. note::
 
-	you need to install pybullet with ``pip install pybullet``
+	you need to install pybullet envs with ``pip install pybullet_envs_gymnasium``
 
 
 .. image:: ../_static/img/colab-badge.svg
@@ -413,44 +413,41 @@ will compute a running average and standard deviation of input features (it can
 
 .. code-block:: python
 
-  import os
-  import gymnasium as gym
-  import pybullet_envs
+    from pathlib import Path
 
-  from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
-  from stable_baselines3 import PPO
+    import pybullet_envs_gymnasium
+
+    from stable_baselines3.common.vec_env import VecNormalize
+    from stable_baselines3.common.env_util import make_vec_env
+    from stable_baselines3 import PPO
+
+    # Alternatively, you can use the MuJoCo equivalent "HalfCheetah-v4"
+    vec_env = make_vec_env("HalfCheetahBulletEnv-v0", n_envs=1)
+    # Automatically normalize the input features and reward
+    vec_env = VecNormalize(vec_env, norm_obs=True, norm_reward=True, clip_obs=10.0)
+
+    model = PPO("MlpPolicy", vec_env)
+    model.learn(total_timesteps=2000)
+
+    # Don't forget to save the VecNormalize statistics when saving the agent
+    log_dir = Path("/tmp/")
+    model.save(log_dir / "ppo_halfcheetah")
+    stats_path = log_dir / "vec_normalize.pkl"
+    vec_env.save(stats_path)
+
+    # To demonstrate loading
+    del model, vec_env
+
+    # Load the saved statistics
+    vec_env = make_vec_env("HalfCheetahBulletEnv-v0", n_envs=1)
+    vec_env = VecNormalize.load(stats_path, vec_env)
+    #  do not update them at test time
+    vec_env.training = False
+    # reward normalization is not needed at test time
+    vec_env.norm_reward = False
 
-  # Note: pybullet is not compatible yet with Gymnasium
-  # you might need to use `import rl_zoo3.gym_patches`
-  # and use gym (not Gymnasium) to instantiate the env
-  # Alternatively, you can use the MuJoCo equivalent "HalfCheetah-v4"
-  vec_env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
-  # Automatically normalize the input features and reward
-  vec_env = VecNormalize(vec_env, norm_obs=True, norm_reward=True,
-                     clip_obs=10.)
-
-  model = PPO("MlpPolicy", vec_env)
-  model.learn(total_timesteps=2000)
-
-  # Don't forget to save the VecNormalize statistics when saving the agent
-  log_dir = "/tmp/"
-  model.save(log_dir + "ppo_halfcheetah")
-  stats_path = os.path.join(log_dir, "vec_normalize.pkl")
-  vec_env.save(stats_path)
-
-  # To demonstrate loading
-  del model, vec_env
-
-  # Load the saved statistics
-  vec_env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
-  vec_env = VecNormalize.load(stats_path, vec_env)
-  #  do not update them at test time
-  vec_env.training = False
-  # reward normalization is not needed at test time
-  vec_env.norm_reward = False
-
-  # Load the agent
-  model = PPO.load(log_dir + "ppo_halfcheetah", env=vec_env)
+    # Load the agent
+    model = PPO.load(log_dir / "ppo_halfcheetah", env=vec_env)
 
 
 Hindsight Experience Replay (HER)

docs/misc/changelog.rst

@@ -36,7 +36,8 @@ Others:
 
 Documentation:
 ^^^^^^^^^^^^^^
-Added Decisions and Dragons to resources. (@jmacglashan)
+- Added Decisions and Dragons to resources. (@jmacglashan)
+- Updated PyBullet example, now compatible with Gymnasium
 
 Release 2.4.0 (2024-11-18)
 --------------------------