readme: ---
  tags:
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  ---
  
  This is a pre-trained model of a PPO agent playing CartPole-v1 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
  
  ### Usage (with Stable-baselines3)
  Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
  
  ```
  pip install stable-baselines3
  pip install huggingface_sb3
  ```
  
  Then, you can use the model like this:
  
  ```python
  import gym
  
  from huggingface_sb3 import load_from_hub
  from stable_baselines3 import PPO
  from stable_baselines3.common.evaluation import evaluate_policy
  
  # Retrieve the model from the hub
  ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
  ## filename = name of the model zip file from the repository
  checkpoint = load_from_hub(
      repo_id="sb3/demo-hf-CartPole-v1",
      filename="ppo-CartPole-v1",
  )
  model = PPO.load(checkpoint)
  
  # Evaluate the agent and watch it
  eval_env = gym.make("CartPole-v1")
  mean_reward, std_reward = evaluate_policy(
      model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
  )
  print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
  ```
  
  ### Evaluation Results
  Mean_reward: 500.0
  
  
  