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Preference-Guided-DQN-Atari

👪 A new member of DQN family.

📃 Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration

[TNNLS] | [arXiv]

💫 This work proposes a generalized and efficient epsilon-greedy exploration policy to learn a multimodal distribution that aligns with landscape of the Q value.

🔧 Realized in Ubuntu 20.04 and Pytorch over OpenAI Gym benchamark: Atari Game and Classic Control.

Citation

If you find this repository useful for your research, please consider starring ⭐ our repo and citing our paper.

@ARTICLE{huang2023preference,
  author={Huang, Wenhui and Zhang, Cong and Wu, Jingda and He, Xiangkun and Zhang, Jie and Lv, Chen},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Sampling Efficient Deep Reinforcement Learning Through Preference-Guided Stochastic Exploration}, 
  year={2023},
  volume={},
  number={},
  pages={1-12},
  doi={10.1109/TNNLS.2023.3317628}}

General Info

🚀 An advanced version of this work for addressing autonomous driving decision-making problem can be found in UnaDQN

Autonomous Driving: The performance of PGDQN is also verified through various autonomous driving simulators.

202310091343.mp4

Atari: Pong (6 actions; Green one is RL agent)

Pong_Comp.mp4

Atari: CrazyClimber (9 actions)

CrazyClimber_Comp.mp4

Atari: FishingDerby (18 actions; Left one is RL agent)

FishingDerby_Comp.mp4

Benchmark Comparison (Atari & Classic Control)

User Guide

Clone the repository.

cd to your workspace and clone the repo.

git clone https://github.com/OscarHuangWind/Preference-Guided-DQN-Atari.git

Create a new virtual environment with dependencies.

cd ~/$your workspace/Preference-Guided-DQN-Atari
conda env create -f virtual_env.yml

If you don't use anaconda, then please manually install the dependencies wrote in virtual_env.aml file. You are free to modify your virtual environment name and dependencies in virtual_env.yml file.

Activate virtual environment.

conda activate pgdqn

Install Pytorch

Select the correct version based on your cuda version and device (cpu/gpu):

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

If it shows error that the game is not installed, then please manually install the ROMS.

Download Roms.rar from the Atari 2600 VCS ROM Collection

Extract the .rar file and import them

python -m atari_py.import_roms <path to extracted folder>

PGDQN Training

python main.py

Visualization

Modify the VISUALIZATION parameter in config.yaml file to "True", and run:

python main.py

DQN, DOUBLE_DQN, DUELING_DQN, D3QN, UCB_DQN Training

Modify the corresponding name of the algo in main.py file, and run:

python main.py

Parameters

Feel free to play with the parameters in config.yaml.