readme: ---
  library_name: stable-baselines3
  tags:
  - AntBulletEnv-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: A2C
    results:
    - metrics:
      - type: mean_reward
        value: 1218.38 +/- 203.74
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: AntBulletEnv-v0
        type: AntBulletEnv-v0
  ---
  
  # **A2C** Agent playing **AntBulletEnv-v0**
  This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
  
  ## Usage (with Stable-baselines3)
  ## parameters
  
  ```python
  model = A2C(policy = "MlpPolicy",
              env = env,
              gae_lambda = 0.9,
              gamma = 0.99,
              learning_rate = 0.00096,
              max_grad_norm = 0.5,
              n_steps = 8,
              vf_coef = 0.4,
              ent_coef = 0.0,
              tensorboard_log = "./tensorboard",
              policy_kwargs=dict(
              log_std_init=-2, ortho_init=False),
              normalize_advantage=False,
              use_rms_prop= True,
              use_sde= True,
              verbose=1)
  ...
  ```
  