An Improved Minimax-Q Algorithm Based on Generalized Policy Iteration to Solve a Chaser-Invader Game

Liu, Minsong; Zhu, Yuanheng; Zhao, Dan

doi:10.1109/ijcnn48605.2020.9207446

Cited by 4 publications

(1 citation statement)

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“…Benefitting from the rapid development of RL, it was gradually applied to solve multi-agent system problems. At first, game theory was introduced into MAS and the concept of learning was proposed, which was called the minimax-Q algorithm [28]. Based on this algorithm, many derivative types were studied, such as Nash Q-learning [29], which was mainly used to solve zero-sum differential games.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Fuzzy Actor–Critic Learning via Suboptimal Knowledge for a Multi-Agent Tracking Problem

Wang

Mao

et al. 2023

Electronics

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Multi-agent differential games usually include tracking policies and escaping policies. To obtain the proper policies in unknown environments, agents can learn through reinforcement learning. This typically requires a large amount of interaction with the environment, which is time-consuming and inefficient. However, if one can obtain an estimated model based on some prior knowledge, the control policy can be obtained based on suboptimal knowledge. Although there exists an error between the estimated model and the environment, the suboptimal guided policy will avoid unnecessary exploration; thus, the learning process can be significantly accelerated. Facing the problem of tracking policy optimization for multiple pursuers, this study proposed a new form of fuzzy actor–critic learning algorithm based on suboptimal knowledge (SK-FACL). In the SK-FACL, the information about the environment that can be obtained is abstracted as an estimated model, and the suboptimal guided policy is calculated based on the Apollonius circle. The guided policy is combined with the fuzzy actor–critic learning algorithm, improving the learning efficiency. Considering the ground game of two pursuers and one evader, the experimental results verified the advantages of the SK-FACL in reducing tracking error, adapting model error and adapting to sudden changes made by the evader compared with pure knowledge control and the pure fuzzy actor–critic learning algorithm.

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Section: Introductionmentioning

confidence: 99%