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--- |
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license: apache-2.0 |
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tags: |
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--- |
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# DQN |
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DQN model applied to the this discrete environments CartPole-v1 |
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## Model Description |
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The model was trained from the CleanRl library using the DQN algorithm on CartPole-v1 |
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## Intended Use & Limitation |
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The model is intended to be used for the following environments CartPole-v1 |
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and understand the implication of Quantization on this type of model from a pretrained state |
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## Training Procdure |
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### Training Hyperparameters |
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The folloing hyperparameters were used during training: |
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- exp_name: functional_dqn |
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- seed: 0 |
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- torch_deterministic: True |
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- cuda: False |
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- track: True |
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- wandb_project_name: cleanRL |
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- wandb_entity: compress_rl |
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- capture_video: False |
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- env_id: CartPole-v1 |
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- total_timesteps: 500000 |
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- learning_rate: 0.00025 |
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- buffer_size: 10000 |
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- gamma: 0.99 |
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- target_network_frequency: 500 |
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- batch_size: 128 |
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- start_e: 1 |
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- end_e: 0.05 |
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- exploration_fraction: 0.5 |
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- learning_starts: 10000 |
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- train_frequency: 10 |
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- optimizer: Adan |
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- max_grad_norm: 0.0 |
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- weight_decay: 0.02 |
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- opt_eps: None |
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- opt_betas: None |
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- no_prox: False |
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- wandb_project: cleanrl |
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### Framework and version |
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Pytorch 1.12.1+cu102 |
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gym 0.23.1 |
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Weights and Biases 0.13.3 |
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Hugging Face Hub 0.11.1 |
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Python Version 3.8.16 (default, Dec 7 2022, 01:12:13) |
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[GCC 7.5.0] |
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## Citation |
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```bibtex |
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``` |
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