Optimized Feature Extraction for Sample Efficient Deep Reinforcement Learning

Optimized Feature Extraction for Sample Efficient Deep Reinforcement Learning

In deep reinforcement learning, agent exploration still has certain limitations, while low efficiency exploration further leads to the problem of low sample efficiency. In order to solve the exploration dilemma caused by white noise interference and the separation derailment problem in the environme...

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Bibliographic Details
Main Authors: Yuangang Li, Tao Guo, Qinghua Li, Xinyue Liu
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Electronics
Subjects:
deep reinforcement learning
feature optimization
auxiliary agent
sample efficiency
exploration strategy
Online Access:https://www.mdpi.com/2079-9292/12/16/3508

Internet

https://www.mdpi.com/2079-9292/12/16/3508

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