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
Cite
@article{arxiv.1905.09492,
title = {Combine PPO with NES to Improve Exploration},
author = {Lianjiang Li and Yunrong Yang and Bingna Li},
journal= {arXiv preprint arXiv:1905.09492},
year = {2019}
}