Adaptive Optimization in Evolutionary Reinforcement Learning Using Evolutionary Mutation Rates

Deep reinforcement learning (DRL) has achieved notable success in continuous control tasks. However, it faces challenges that limit its applicability to a wider array of tasks, including sparse rewards and limited exploration. Recently, the integration of evolutionary algorithms (EAs) with deep rein...

Full description

Saved in:
Bibliographic Details
Main Authors: Y. Zhao, Y. Ding, Y. Pei
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10746473/
Tags: Add Tag
No Tags, Be the first to tag this record!