Combine PPO with NES to Improve Exploration

Lianjiang Li; Yunrong Yang; Bingna Li

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arXiv · 1905.09492

Combine PPO with NES to Improve Exploration

Lianjiang Li·Yunrong Yang·Bingna Li

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Abstract

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

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cs.LG cs.NE stat.ML

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Lianjiang Li, Yunrong Yang, Bingna Li. 2019-05-23. Combine PPO with NES to Improve Exploration. https://arxiv.org/abs/1905.09492

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