Derivatives of logarithmic stationary distributions for policy gradient reinforcement learning.
Most conventional policy gradient reinforcement learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the policy parameter. That term involves the derivative of the stationary state distribution that corresponds to the sensitivity of its distribution to changes in the policy parameter. Although the bias introduced by this omission can be reduced by setting the forgetting rate gamma for the value functions close to 1, these algorithms do not permit gamma to be set exactly at gamma = 1. In this article, we propose a method for estimating the log stationary state distribution derivative (LSD) as a useful form of the derivative of the stationary state distribution through backward Markov chain formulation and a temporal difference learning framework. A new policy gradient (PG) framework with an LSD is also proposed, in which the average reward gradient can be estimated by setting gamma = 0, so it becomes unnecessary to learn the value functions. We also test the performance of the proposed algorithms using simple benchmark tasks and show that these can improve the performances of existing PG methods.
IBM Research-Tokyo, Yamato, Kanagawa 242-8502, Japan. tetsuro@jp.ibm.com
Tetsuro
T
Morimura
Eiji
E
Uchibe
Junichiro
J
Yoshimoto
Jan
J
Peters
Kenji
K
Doya
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D055641:Mathematical Concepts; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology; D012201:Reward