# Combine PPO with NES to Improve Exploration

## TL;DR

This paper proposes combining neural evolution strategy (NES) with proximal policy optimization (PPO) through parameter transfer and parameter space noise to enhance exploration in reinforcement learning.

## Contribution

It introduces two novel methods for integrating NES with PPO, improving exploration capabilities in RL tasks.

## Key findings

- PPO benefits from parameter transfer and space noise methods.
- Enhanced exploration leads to better performance in discrete and continuous environments.
- Experimental results show improved learning efficiency.

## 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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Source: https://tomesphere.com/paper/1905.09492