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FMR-GA -- A cooperative multi-agent reinformcement learning algorithm based on gradient ascent
Zhen Zhang1; Dongqing Wang1; Dongbin Zhao2; Tingting Song1
Source PublicationPart of the Lecture Notes in Computer Science book series (LNCS, volume 10634)
2017
Issue*Pages:840–848
Abstract     Gradient ascent methods combined with Multi-Agent Reinforcement Learning (MARL) have been studied for years as a potential direction to design new MARL algorithms. This paper proposes a gradient-based MARL algorithm – Frequency of the Maximal Reward based on Gradient Ascent (FMR-GA). The aim is to reach the maximal total reward in repeated games. To achieve this goal and simplify the stability analysis procedure, we have made effort in two aspects. Firstly, the probability of getting the maximal total reward is selected as the objective function, which simplifies the expression of the gradient and facilitates reaching the learning goal. Secondly, a factor is designed and is added to the gradient. This will produce the desired stable critical points corresponding to the optimal joint strategy. We propose a MARL algorithm called Probability of Maximal Reward based on Infinitsmall Gradient Ascent (PMR-IGA), and analyze its convergence in two-player two-action and two-player three-action repeated games. Then we derive a practical MARL algorithm FMR-GA from PMR-IGA. Theoretical and simulation results show that FMR-GA will converge to the optimal strategy in the cases presented in this paper
KeywordReinforcement Learning Multi-agent Gradient Ascent Q-learning
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19420
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Affiliation1.School of Automation and Electrical EngineeringQingdao UniversityQingdaoChina
2.State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
Recommended Citation
GB/T 7714
Zhen Zhang,Dongqing Wang,Dongbin Zhao,et al. FMR-GA -- A cooperative multi-agent reinformcement learning algorithm based on gradient ascent[J]. Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634),2017(*):840–848.
APA Zhen Zhang,Dongqing Wang,Dongbin Zhao,&Tingting Song.(2017).FMR-GA -- A cooperative multi-agent reinformcement learning algorithm based on gradient ascent.Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)(*),840–848.
MLA Zhen Zhang,et al."FMR-GA -- A cooperative multi-agent reinformcement learning algorithm based on gradient ascent".Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634) .*(2017):840–848.
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