Add code

README.md

@@ -24,6 +24,100 @@ model-index:
   This is a trained model of a **A2C** agent playing **Pendulum-v1**
   using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
   
-  ## Usage (with Stable-baselines3)
-  TODO: Add your code
+## Usage (with Stable-baselines3)
+
+```python
+from huggingface_sb3 import load_from_hub
+from stable_baselines3 import A2C
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.vec_env import VecNormalize
+
+# Download checkpoint and stats
+env_id = "Pendulum-v1"
+checkpoint = load_from_hub(f"araffin/a2c-{env_id}", f"a2c-{env_id}.zip")
+vec_normalize_stats = load_from_hub(f"araffin/a2c-{env_id}", f"vec_normalize.pkl")
+
+# Load the model
+model = A2C.load(checkpoint)
+
+env = make_vec_env(env_id, n_envs=1)
+env = VecNormalize.load(vec_normalize_stats, env)
+#  do not update them at test time
+env.training = False
+# reward normalization is not needed at test time
+env.norm_reward = False
+
+# Evaluate
+print("Evaluating model")
+mean_reward, std_reward = evaluate_policy(
+    model,
+    env,
+    n_eval_episodes=20,
+    deterministic=True,
+)
+print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
+
+# Start a new episode
+obs = env.reset()
+
+try:
+    while True:
+        action, _states = model.predict(obs, deterministic=True)
+        obs, rewards, dones, info = env.step(action)
+        env.render()
+except KeyboardInterrupt:
+    pass
+```
+
+## Training Code
+
+```python
+from huggingface_sb3 import package_to_hub
+from stable_baselines3 import A2C
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.vec_env import VecNormalize, sync_envs_normalization
+
+# Create the environment
+env_id = "Pendulum-v1"
+env = make_vec_env(env_id, n_envs=8)
+# Normalize
+env = VecNormalize(env, gamma=0.9)
+
+# Create the evaluation env (could be used in `EvalCallback`)
+eval_env = make_vec_env(env_id, n_envs=1)
+eval_env = VecNormalize(eval_env, gamma=0.9, training=False, norm_reward=False)
+
+# Instantiate the agent
+model = A2C(
+    "MlpPolicy",
+    env,
+    n_steps=8,
+    gamma=0.9,
+    gae_lambda=0.9,
+    use_sde=True,
+    policy_kwargs=dict(log_std_init=-2),
+    verbose=1,
+)
+
+# Train the agent
+try:
+    model.learn(total_timesteps=int(1e6))
+except KeyboardInterrupt:
+    pass
+
+
+# Synchronize stats (done automatically in `EvalCallback`)
+sync_envs_normalization(env, eval_env)
+
+package_to_hub(
+    model=model,
+    model_name=f"a2c-{env_id}",
+    model_architecture="A2C",
+    env_id=env_id,
+    eval_env=eval_env,
+    repo_id=f"araffin/a2c-{env_id}",
+    commit_message="Initial commit",
+)
+```