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--- |
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library_name: stable-baselines3 |
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tags: |
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- MountainCar-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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- 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: MountainCar-v0 |
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type: MountainCar-v0 |
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metrics: |
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- type: mean_reward |
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value: -116.20 +/- 1.83 |
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name: mean_reward |
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verified: false |
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--- |
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# **PPO** Agent playing **MountainCar-v0** |
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This is a trained model of a **PPO** agent playing **MountainCar-v0** |
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
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# Model Details |
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- Model Name: ppo-MountainCar-v0 |
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- Model Type: Proximal Policy Optimization (PPO) |
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- Policy Architecture: MultiLayerPerceptron (MLPPolicy) |
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- Environment: MountainCar-v0 |
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- Training Data: The model was trained using three consecutive training sessions: |
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- First training session: Total timesteps = 1,000,000 |
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- Second training session: Total timesteps = 500,000 |
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- Third training session: Total timesteps = 500,000 |
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# Model Parameters |
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```python |
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- n_steps: 2048 |
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- batch_size: 64 |
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- n_epochs: 8 |
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- gamma: 0.999 |
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- gae_lambda: 0.95 |
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- ent_coef: 0.01 |
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- max_grad_norm: 0.5 |
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- Verbose: Enabled (Verbose level = 1) |
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``` |
