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DQN-PyTorch

A PyTorch implementation of Human-level control through deep reinforcement learning

Table of Contents:

• DQN-PyTorch
  • Project Structure
  • Environments
    • CartPole V0
  • Usage
  • Requirements
  • Future Work
  • References
  • License

Project Structure:

├── agents
|  └── dqn.py # the main training agent for the dqn
├── graphs
|  └── models
|  |  └── dqn.py
|  └── losses
|  |  └── huber_loss.py # contains huber loss definition
├── datasets  # contains all dataloaders for the project
├── utils # utilities folder containing input extraction, replay memory, config parsing, etc
|  └── assets
|  └── replay_memory.py
|  └── env_utils.py
├── main.py
└── run.sh

Environments:

CartPole V0:

Loss during training:

Number of durations per Episode:

Usage:

• To run the project, you need to add your configurations into the folder configs/ as fround here
• sh run.sh
• To run on a GPU, you need to enable cuda in the config file.

Requirements:

• Pytorch: 0.4.0
• torchvision: 0.2.1
• tensorboardX: 0.8

Check requirements.txt.

Future Work:

• Test DQN on a more complex environment such as MS-Pacman

References:

• Pytorch official example: https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
• Pytorch-dqn: https://github.com/transedward/pytorch-dqn/blob/master/dqn_learn.py

License:

This project is licensed under MIT License - see the LICENSE file for details.