Combine PPO with NES to Improve Exploration

arXiv:1905.09492v1 [cs.LG] 23 May 2019 We introduce two approaches for combining neural evolution strategy (NES) and proximal policy optimization (PPO): parameter transfer and parameter space noise. Parameter transfer is a PPO agent with parameters transferred from a NES agent. Parameter space noise...

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Bibliographic Details
Main Authors Li, Lianjiang, Yang, Yunrong, Li, Bingna
Format Journal Article
LanguageEnglish
Published 23.05.2019
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Summary:arXiv:1905.09492v1 [cs.LG] 23 May 2019 We introduce two approaches for combining neural evolution strategy (NES) and proximal policy optimization (PPO): parameter transfer and parameter space noise. Parameter transfer is a PPO agent with parameters transferred from a NES agent. Parameter space noise is to directly add noise to the PPO agent`s parameters. We demonstrate that PPO could benefit from both methods through experimental comparison on discrete action environments as well as continuous control tasks
DOI:10.48550/arxiv.1905.09492