A Multi-Agent Centralized Strategy Gradient Reinforcement Learning Algorithm Based on State Transition

A Multi-Agent Centralized Strategy Gradient Reinforcement Learning Algorithm Based on State Transition

The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge in achieving stable and high-performance cooperative behavior. Addressing the need for the balanced exploration and exploitation...

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Bibliographic Details
Main Authors: Lei Sheng, Honghui Chen, Xiliang Chen
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Algorithms
Subjects:
automatic representation
state transition
exploration
exploitation
multi-agent reinforcement learning
deterministic strategy gradient algorithm
Online Access:https://www.mdpi.com/1999-4893/17/12/579

Internet

https://www.mdpi.com/1999-4893/17/12/579

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