README.md

Python implementation of Lifelong Reinforcement Learning (Lifelong RL/LLRL).

SR-LLRL

Shaping Rewards for LifeLong Reinforcement Learning

Brief Introduction

Codes for experimenting with proposed approaches to Lifelong RL, attached to our 2021 IEEE SMC paper "Accelerating lifelong reinforcement learning via reshaping rewards".

Authors: Kun Chu, Xianchao Zhu, William Zhu.

If you use these codes, please cite our paper

K. Chu, X. Zhu and W. Zhu, "Accelerating Lifelong Reinforcement Learning via Reshaping Rewards*," 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 619-624, doi: 10.1109/SMC52423.2021.9659064.

BibTeX Style Citation

@INPROCEEDINGS{
    author={Chu, Kun and Zhu, Xianchao and Zhu, William},  
    booktitle={2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},   
    title={Accelerating Lifelong Reinforcement Learning via Reshaping Rewards},   
    year={2021},  
    pages={619-624},  
    doi={10.1109/SMC52423.2021.9659064}
    }

Usage

To generate experiemental results, run main.py;

To draw all of our plots, run result_show_task.py and result_show_episode.py.

Note that you must choose your learning algorithms or parameters inside the code to generate results/figures.

Important Note

These codes need to import some libraries of python, especially simple_rl provided by David Abel. However, please note that I have made some improvements and changes based on his codes, so please download the simple_rl inside the fold directly instead of installing from the python official libraries.

Experimental Demonstration

Acknowledgment

Here I want to sincerely thank David Abel, a great young scientist. He generously shared the source code of his paper in Github and gave detailed answers to any of my questions/doubts in the process of conducting this research. I admire his academic achievements, and more importantly, his enthusiastic help and scientific spirit.

Last

Feel free to contact me (kun_chu@outlook.com) with any questions.