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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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
23.05.2019
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Subjects | |
Online Access | Get full text |
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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 |
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DOI: | 10.48550/arxiv.1905.09492 |