MAQD: Cooperative Multi-Agent Reinforcement Learning Q-value Decomposition in Actor-Critic Framework

Zou, Qijie; Jiang, Yajun; Li, Wenxue; Gao, Bing; Li, Dan; He, Mingyan

doi:10.1109/cac53003.2021.9728098

Cited by 1 publication

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“…This paper introduces the system, research methods, and the application of game theory in MARL, and analyzes the challenges and opportunities faced by MARL. The original swarm intelligence algorithm was inspired by bionics [1], and the single-agent intelligent decision was born. However, single-intelligence decision making became insufficient for complex problems [2], so multi-intelligence decision making began to develop, and the combination of multi-intelligence learning with RL continued to optimize decisions, enabling MARL to be applied to more complex and variable tasks.…”

Section: Introductionmentioning

confidence: 99%

Overview on reinforcement learning of multi-agent game

Zou

2023

J. Phys.: Conf. Ser.

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Game intelligence is an emerging hot topic in the field of artificial intelligence in recent years, and multi-agent learning is a frontier topic in the field of the intelligent game, which has a huge development prospect in all fields. This paper introduces the origin of reinforcement learning (RL) from the law of effect in animal experimental psychology and the optimization theory of optimal control. Then, the author describes the systematic composition of multi-agent reinforcement learning (MARL), and summarizes the classification of its research methods. The existing problems of MARL are discussed from three aspects: non-stationarity of the environment, partial observability, and the dimensional explosion problem. Finally, an outlook on the future is given based on the current development status of MARL and the important and difficult issues in the research field.

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Section: Introductionmentioning

confidence: 99%

Overview on reinforcement learning of multi-agent game

Zou

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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MAQD: Cooperative Multi-Agent Reinforcement Learning Q-value Decomposition in Actor-Critic Framework

Cited by 1 publication

References 6 publications

Overview on reinforcement learning of multi-agent game

Overview on reinforcement learning of multi-agent game

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