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JACIII Vol.24 No.6 pp. 711-718
doi: 10.20965/jaciii.2020.p0711
(2020)

Paper:

Direct Policy Search Reinforcement Learning Based on Variational Bayesian Inference

Nobuhiko Yamaguchi

Graduate School of Science and Engineering, Faculty of Science and Engineering, Saga University
1 Honjo, Saga-shi, Saga 840-8502, Japan

Received:
January 22, 2020
Accepted:
July 21, 2020
Published:
November 20, 2020
Keywords:
reinforcement learning, direct policy search, variational inference, reward weighted regression
Abstract
Direct Policy Search Reinforcement Learning Based on Variational Bayesian Inference

Ball batting task

Direct policy search is a promising reinforcement learning framework particularly for controlling continuous, high-dimensional systems. Peters et al. proposed reward-weighted regression (RWR) as a direct policy search. The RWR algorithm estimates the policy parameter based on the expectation-maximization (EM) algorithm and is therefore prone to overfitting. In this study, we focus on variational Bayesian inference to avoid overfitting and propose direct policy search reinforcement learning based on variational Bayesian inference (VBRL). The performance of the proposed VBRL is assessed in several experiments involving a mountain car and a ball batting task. These experiments demonstrate that VBRL yields a higher average return and outperforms the RWR.

Cite this article as:
Nobuhiko Yamaguchi, “Direct Policy Search Reinforcement Learning Based on Variational Bayesian Inference,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.6, pp. 711-718, 2020.
Data files:
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Last updated on Mar. 01, 2021