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@@ -23,6 +23,44 @@ model-index:
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  # **A2C** Agent playing **LunarLander-v2**
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  This is a trained model of a **A2C** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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  ## Training code (with Stable-baselines3)
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  ```python
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  from stable_baselines3 import A2C
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  # **A2C** Agent playing **LunarLander-v2**
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  This is a trained model of a **A2C** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+ ## Usage (with Stable-Baselines3)
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+
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+ ```python
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+ from huggingface_sb3 import load_from_hub
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+ from stable_baselines3 import A2C
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+ from stable_baselines3.common.env_util import make_vec_env
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+ from stable_baselines3.common.evaluation import evaluate_policy
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+
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+ # Download checkpoint
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+ checkpoint = load_from_hub("araffin/a2c-LunarLander-v2", "a2c-LunarLander-v2.zip")
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+ # Load the model
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+ model = A2C.load(checkpoint)
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+
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+ env = make_vec_env("LunarLander-v2", n_envs=1)
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+
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+ # Evaluate
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+ print("Evaluating model")
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+ mean_reward, std_reward = evaluate_policy(
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+ model,
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+ env,
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+ n_eval_episodes=20,
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+ deterministic=True,
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+ )
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+ print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
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+
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+ # Start a new episode
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+ obs = env.reset()
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+
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+ try:
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+ while True:
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+ action, _states = model.predict(obs, deterministic=True)
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+ obs, rewards, dones, info = env.step(action)
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+ env.render()
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+ except KeyboardInterrupt:
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+ pass
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+
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+ ```
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+
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  ## Training code (with Stable-baselines3)
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  ```python
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  from stable_baselines3 import A2C