readme: --- 
  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 
  ```bibtex 
  ``` 
  