Efficient Model-Free Reinforcement Learning Using Gaussian Process
Machine Learning
2018-12-12 v1 Machine Learning
Abstract
Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy. By applying posterior sampling in model-free RL under the hypothesis of GP, we propose Gaussian Process Posterior Sampling Reinforcement Learning(GPPSTD) algorithm in continuous state space, giving theoretical justifications and empirical results. We also provide theoretical and empirical results that various demonstration could lower expected uncertainty and benefit posterior sampling exploration. In this way, we combined the demonstration and exploration process together to achieve a more efficient reinforcement learning.
Cite
@article{arxiv.1812.04359,
title = {Efficient Model-Free Reinforcement Learning Using Gaussian Process},
author = {Ying Fan and Letian Chen and Yizhou Wang},
journal= {arXiv preprint arXiv:1812.04359},
year = {2018}
}
Comments
10 pages