Add training/usage code

README.md

@@ -23,6 +23,93 @@ model-index:
   # **TQC** Agent playing **BipedalWalker-v3**
   This is a trained model of a **TQC** agent playing **BipedalWalker-v3** 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 sb3_contrib import TQC
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.evaluation import evaluate_policy
+
+# Download checkpoint
+checkpoint = load_from_hub("araffin/tqc-BipedalWalker-v3", "tqc-BipedalWalker-v3.zip")
+# Load the model
+model = TQC.load(checkpoint)
+
+env = make_vec_env("BipedalWalker-v3", n_envs=1)
+
+# 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 (with SB3)
+
+```python
+from sb3_contrib import TQC
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.callbacks import EvalCallback
+
+# Create the environment
+env_id = "BipedalWalker-v3"
+n_envs = 2
+env = make_vec_env(env_id, n_envs=n_envs)
+
+# Create the evaluation envs
+eval_envs = make_vec_env(env_id, n_envs=5)
+
+# Adjust evaluation interval depending on the number of envs
+eval_freq = int(1e5)
+eval_freq = max(eval_freq // n_envs, 1)
+
+# Create evaluation callback to save best model
+# and monitor agent performance
+eval_callback = EvalCallback(
+    eval_envs,
+    best_model_save_path="./logs/",
+    eval_freq=eval_freq,
+    n_eval_episodes=10,
+)
+
+# Instantiate the agent
+# Hyperparameters from https://github.com/DLR-RM/rl-baselines3-zoo
+model = TQC(
+    "MlpPolicy",
+    env,
+    learning_starts=10000,
+    batch_size=256,
+    buffer_size=300000,
+    learning_rate=7.3e-4,
+    use_sde=True,
+    train_freq=8,
+    gradient_steps=8,
+    gamma=0.98,
+    tau=0.02,
+    policy_kwargs=dict(log_std_init=-3, net_arch=[400, 300]),
+    verbose=1,
+)
+
+# Train the agent (you can kill it before using ctrl+c)
+try:
+    model.learn(total_timesteps=int(5e5), callback=eval_callback, log_interval=10)
+except KeyboardInterrupt:
+    pass
+```