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