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--- |
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library_name: stable-baselines3 |
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tags: |
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- CarRacing-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: 205.45 +/- 120.65 |
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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: CarRacing-v0 |
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type: CarRacing-v0 |
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--- |
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# **PPO** Agent playing **CarRacing-v0** |
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This is a trained model of a **PPO** agent playing **CarRacing-v0** |
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) |
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). |
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The RL Zoo is a training framework for Stable Baselines3 |
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reinforcement learning agents, |
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with hyperparameter optimization and pre-trained agents included. |
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## Usage (with SB3 RL Zoo) |
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RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> |
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SB3: https://github.com/DLR-RM/stable-baselines3<br/> |
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SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib |
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``` |
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# Download model and save it into the logs/ folder |
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python -m utils.load_from_hub --algo ppo --env CarRacing-v0 -orga Chris1 -f logs/ |
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python enjoy.py --algo ppo --env CarRacing-v0 -f logs/ |
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``` |
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## Training (with the RL Zoo) |
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``` |
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python train.py --algo ppo --env CarRacing-v0 -f logs/ |
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# Upload the model and generate video (when possible) |
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python -m utils.push_to_hub --algo ppo --env CarRacing-v0 -f logs/ -orga Chris1 |
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``` |
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## Hyperparameters |
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```python |
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OrderedDict([('batch_size', 128), |
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('clip_range', 0.2), |
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('ent_coef', 0.0), |
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('env_wrapper', |
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[{'utils.wrappers.FrameSkip': {'skip': 2}}, |
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{'gym.wrappers.resize_observation.ResizeObservation': {'shape': 64}}, |
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{'gym.wrappers.gray_scale_observation.GrayScaleObservation': {'keep_dim': True}}]), |
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('frame_stack', 2), |
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('gae_lambda', 0.95), |
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('gamma', 0.99), |
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('learning_rate', 'lin_1e-4'), |
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('max_grad_norm', 0.5), |
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('n_envs', 8), |
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('n_epochs', 10), |
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('n_steps', 512), |
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('n_timesteps', 4000000.0), |
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('normalize', "{'norm_obs': False, 'norm_reward': True}"), |
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('policy', 'CnnPolicy'), |
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('policy_kwargs', |
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'dict(log_std_init=-2, ortho_init=False, activation_fn=nn.GELU, ' |
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'net_arch=[dict(pi=[256], vf=[256])], )'), |
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('sde_sample_freq', 4), |
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('use_sde', True), |
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('vf_coef', 0.5), |
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('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})]) |
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``` |
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