readme: ---
  library_name: stable-baselines3
  tags:
  - MountainCarContinuous-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: PPO
    results:
    - metrics:
      - type: mean_reward
        value: -0.00 +/- 0.00
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: MountainCarContinuous-v0
        type: MountainCarContinuous-v0
  ---
  
  # **PPO** Agent playing **MountainCarContinuous-v0**
  This is a trained model of a **PPO** agent playing **MountainCarContinuous-v0**
  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
  
  ## Usage (with Stable-baselines3)
  
  ```python
  from stable_baselines3 import PPO
  from huggingface_sb3 import load_from_hub
  
  # load and create the model
  model_path = load_from_hub("danieladejumo/ppo-mountan_car_continuous", 
                             "ppo-mountan_car_continuous.zip")
  model = PPO.load(model_path)
  
  # create Mountain Car Continuous environment and evaluate the trained agent
  env = gym.make("MountainCarContinuous-v0")
  mean_return, std_return = evaluate_policy(model, env, n_eval_episodes=50, deterministic=True)
  ```
  