arXiv Open Access 2019

Combine PPO with NES to Improve Exploration

Lianjiang Li Yunrong Yang Bingna Li

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Abstrak

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

Topik & Kata Kunci

cs.LG cs.NE stat.ML

Penulis (3)

Lianjiang Li

Yunrong Yang

Bingna Li

Format Sitasi

APA MLA BibTeX

Li, L., Yang, Y., Li, B. (2019). Combine PPO with NES to Improve Exploration. https://arxiv.org/abs/1905.09492

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓