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