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Compatible natural gradient policy search

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
JournalMachine Learning
DOIs
Publication statusE-pub ahead of print - 2019
Publication typeA1 Journal article-refereed

Abstract

Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a standard exponential policy distribution in combination with compatible value function approximation. Moreover, we show that standard natural gradient updates may reduce the entropy of the policy according to a wrong schedule leading to premature convergence. To control entropy reduction we introduce a new policy search method called compatible policy search (COPOS) which bounds entropy loss. The experimental results show that COPOS yields state-of-the-art results in challenging continuous control tasks and in discrete partially observable tasks.

ASJC Scopus subject areas

Keywords

  • Policy search, Reinforcement learning

Publication forum classification

Field of science, Statistics Finland