Scalable Lifelong Reinforcement Learning

This repo contains code accompaning the manuscript: Zhi Wang, Chunlin Chen, and Daoyi Dong, "A Dirichlet Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning", IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2022.3170485, 2022. It contains code for running the lifelong learning tasks, including 2D navigation, Reacher, and Hopper domains.

Dependencies

This code requires the following:

• python 3.5+
• pytorch 0.4+
• gym
• MuJoCo license

Data

• For the 2D navigation domains, data is generated from envs/navigation.py
• For the Hopper/HalfCheetah/Ant Mujoco domains, the modified Mujoco enviornments are in envs/mujoco/*

Usage

• For example, to run the code in the 2D navigation domain, just run the bash script navi_v1.sh, also see the usage instructions in the python scripts main_sllrl.py and `main_baselines.py'.
• When getting the results in output/*/*.npy files, plot the results using plot_results.py. For example, the result for navi_v1.sh is:

performance comparison	clustering visualization

Contact

To ask questions or report issues, please open an issue on the issues tracker, or email to zhiwang@nju.edu.cn.