A Model-Free Deep Reinforcement Learning Algorithm for Solving Multi-Agent Nash Equilibrium With Unstable Communication

Most reinforcement learning (RL) algorithms proposed to solve Nash equilibrium in multi-agent systems assume stable communication conditions or rely on accurate models of the environment. However, these assumptions are often unrealistic in practical applications since communication is not always sta...

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Vydáno v:	IEEE access Ročník 13; s. 43973 - 43980
Hlavní autoři:	Jiang, Yuannan, Jiang, Shengming, Wang, Xiaofeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptation models Algorithms communication Convergence Data exchange Deep learning Heuristic algorithms Machine learning Mathematical models model-free Multi-agent Multi-agent systems Multiagent systems Nash equilibrium Real time reinforcement learning (RL) Stability analysis Synchronization Uncertainty Vectors
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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