eyounx / PRR

Meta-Reinforcement Learning with Policy Residual Representation

PRR

PRR code for the paper

WenJi Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, Zhi-Hua Zhou. Reinforcement Learning Experience Reuse with Policy Residual Representation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China.

Requirement

• python 3.6
• rllab
• numpy == 1.16
• tensorflow==1.8.0
• gym==0.13.0
• scipy==1.3.0
• matplotlib==3.1.1
• theano==1.0.4
• cached_property==1.5.1

Files

• main.py is the PRR demo on three FetchTheKey environments in the paper
• envs contains test environment
• nets network structures (modified from https://github.com/pat-coady/trpo)
• rl contains agent and policy codes

Run Demo

• To run experiment on FetchTheKey environment, just running:

python main.py