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license: apache-2.0 tags:

DQN

DQN model applied to the this discrete environments CartPole-v1

Model Description

The model was trained from the CleanRl library using the DQN algorithm on CartPole-v1

Intended Use & Limitation

The model is intended to be used for the following environments CartPole-v1 and understand the implication of Quantization on this type of model from a pretrained state

Training Procdure

Training Hyperparameters

The folloing hyperparameters were used during training:

  • exp_name: functional_dqn
  • seed: 0
  • torch_deterministic: True
  • cuda: False
  • track: True
  • wandb_project_name: cleanRL
  • wandb_entity: compress_rl
  • capture_video: False
  • env_id: CartPole-v1
  • total_timesteps: 500000
  • learning_rate: 0.00025
  • buffer_size: 10000
  • gamma: 0.99
  • target_network_frequency: 500
  • batch_size: 128
  • start_e: 1
  • end_e: 0.05
  • exploration_fraction: 0.5
  • learning_starts: 10000
  • train_frequency: 10
  • optimizer: Adan
  • max_grad_norm: 0.0
  • weight_decay: 0.02
  • opt_eps: None
  • opt_betas: None
  • no_prox: False
  • wandb_project: cleanrl

Framework and version

Pytorch 1.12.1+cu102

gym 0.23.1 Weights and Biases 0.13.3 Hugging Face Hub 0.11.1 Python Version 3.8.16 (default, Dec 7 2022, 01:12:13) [GCC 7.5.0]

Citation


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