--- 
license: apache-2.0
tags:
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# 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 
```