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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: PPO |
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results: |
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- metrics: |
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- type: mean_reward |
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value: 2447.40 +/- 23.14 |
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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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# **PPO** Agent playing **AntBulletEnv-v0** |
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This is a trained model of a **PPO** 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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```python |
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from stable_baselines3 import ... |
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from huggingface_sb3 import load_from_hub |
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... |
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``` |
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MODEL |
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model = PPO(policy = "MlpPolicy", |
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env = env, |
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batch_size = 256, |
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clip_range = 0.4, |
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ent_coef = 0.0, |
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gae_lambda = 0.92, |
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gamma = 0.99, |
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learning_rate = 3.0e-05, |
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max_grad_norm = 0.5, |
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n_epochs = 30, |
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n_steps = 512, |
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policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256, |
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256], vf=[256, 256])] ), |
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use_sde = True, |
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sde_sample_freq = 4, |
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vf_coef = 0.5, |
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tensorboard_log = "./tensorboard", |
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verbose=1) |
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model.learn(1_000_000) |
