| --- |
| 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) |
| ... |
| ``` |
|
|
