readme: ---
  library_name: stable-baselines3
  tags:
  - Pendulum-v1
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: A2C
    results:
    - metrics:
      - type: mean_reward
        value: -141.19 +/- 122.27
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: Pendulum-v1
        type: Pendulum-v1
  ---
  
    # **A2C** Agent playing **Pendulum-v1**
    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)
  
  ```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",
  )
  ```
    