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- W4308695740 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." @default.
- W4308695740 created "2022-11-14" @default.
- W4308695740 creator A5072261728 @default.
- W4308695740 date "2022-11-11" @default.
- W4308695740 modified "2023-09-26" @default.
- W4308695740 title "Cooperative and competitive multi-agent deep reinforcement learning" @default.
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- W4308695740 doi "https://doi.org/10.1117/12.2641830" @default.
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