readme: ---
  library_name: stable-baselines3
  tags:
  - AntBulletEnv-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: PPO
    results:
    - metrics:
      - type: mean_reward
        value: 2447.40 +/- 23.14
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: AntBulletEnv-v0
        type: AntBulletEnv-v0
  ---
  
  # **PPO** Agent playing **AntBulletEnv-v0**
  This is a trained model of a **PPO** agent playing **AntBulletEnv-v0**
  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
  
  ## Usage (with Stable-baselines3)
  
  ```python
  from stable_baselines3 import ...
  from huggingface_sb3 import load_from_hub
  
  ...
  ```
  MODEL
  model = PPO(policy = "MlpPolicy",
              env = env,
              batch_size = 256,
              clip_range = 0.4,
              ent_coef = 0.0,
              gae_lambda = 0.92,
              gamma = 0.99,
              learning_rate = 3.0e-05,
              max_grad_norm = 0.5,
              n_epochs = 30,
              n_steps = 512,
              policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
        256], vf=[256, 256])] ),
              use_sde = True,
              sde_sample_freq = 4,
              vf_coef = 0.5,
              tensorboard_log = "./tensorboard",
              verbose=1)
              
  model.learn(1_000_000)