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10 November 2022 Cooperative and competitive multi-agent deep reinforcement learning
Ruizhi Chen
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Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 1234829 (2022) https://doi.org/10.1117/12.2641830
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China
Abstract
Multi-agent reinforcement learning (MARL) is an area of artificial intelligence that investigates joint behaviors of multiple individual agents and emergent patterns arising from their interactions with a common environment. Although MARL has a long history of decades, it begins to intensify recently due to the breakthrough of deep learning methods. In recent years Deep reinforcement learning (DRL) has achieved significant progress in single-agent reinforcement learning problems. Meanwhile, multi-agent systems (MASs) also benefit a lot from DRL methods. Latest advances occur in areas including video games, robot system, smart grids, etc. This article mostly focuses on recent papers on Multi-agent deep reinforcement learning (MADRL). First, some background knowledge of DRL and MARL is introduced. Both value-based and policy-based DRL algorithms are discussed. Second, representative works in both cooperative and competitive scenarios are reviewed respectively. Key ideas and main techniques in each work are discussed. Lastly, the paper draws a conclusion and some potential research directions are proposed.
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Ruizhi Chen "Cooperative and competitive multi-agent deep reinforcement learning", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 1234829 (10 November 2022); https://doi.org/10.1117/12.2641830
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KEYWORDS
Machine learning
Neural networks
Artificial intelligence
Intelligence systems
Stochastic processes
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