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--- |
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tags: |
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- Pitfall-v5 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- custom-implementation |
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library_name: cleanrl |
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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: Pitfall-v5 |
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type: Pitfall-v5 |
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metrics: |
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- type: mean_reward |
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value: -30.00 +/- 45.83 |
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name: mean_reward |
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verified: false |
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--- |
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# (CleanRL) **PPO** Agent Playing **Pitfall-v5** |
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This is a trained model of a PPO agent playing Pitfall-v5. |
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The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be |
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found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py). |
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## Get Started |
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To use this model, please install the `cleanrl` package with the following command: |
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``` |
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pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]" |
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python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Pitfall-v5 |
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``` |
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Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. |
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## Command to reproduce the training |
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```bash |
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curl -OL https://huggingface.co/cleanrl/Pitfall-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py |
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curl -OL https://huggingface.co/cleanrl/Pitfall-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml |
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curl -OL https://huggingface.co/cleanrl/Pitfall-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock |
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poetry install --all-extras |
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python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Pitfall-v5 --seed 1 |
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``` |
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# Hyperparameters |
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```python |
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{'anneal_lr': True, |
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'async_batch_size': 16, |
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'batch_size': 2048, |
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'capture_video': False, |
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'clip_coef': 0.1, |
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'cuda': True, |
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'ent_coef': 0.01, |
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'env_id': 'Pitfall-v5', |
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'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado', |
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'gae': True, |
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'gae_lambda': 0.95, |
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'gamma': 0.99, |
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'hf_entity': 'cleanrl', |
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'learning_rate': 0.00025, |
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'max_grad_norm': 0.5, |
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'minibatch_size': 1024, |
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'norm_adv': True, |
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'num_envs': 64, |
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'num_minibatches': 2, |
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'num_steps': 32, |
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'num_updates': 24414, |
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'save_model': True, |
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'seed': 1, |
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'target_kl': None, |
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'torch_deterministic': True, |
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'total_timesteps': 50000000, |
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'track': True, |
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'update_epochs': 2, |
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'upload_model': True, |
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'vf_coef': 0.5, |
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'wandb_entity': None, |
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'wandb_project_name': 'envpool-atari'} |
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``` |
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