Peer Incentive Reinforcement Learning for Cooperative Multiagent Games
文献类型：期刊论文
| 作者 | Zhang, Tianle1,2 ; Liu, Zhen1,2; Pu, Zhiqiang1,2; Yi, Jianqiang1,2
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| 刊名 | IEEE TRANSACTIONS ON GAMES
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 15期号:4页码:623-636 |
| 关键词 | Cooperative multiagent games intrinsic reward multiagent reinforcement learning (MARL) Starcraft II Micromanagement |
| ISSN号 | 2475-1502 |
| DOI | 10.1109/TG.2022.3196925 |
| 通讯作者 | Liu, Zhen(liuzhen@ia.ac.cn) |
| 英文摘要 | Social learning, especially social incentives, is extremely important for humans to achieve a high level of coordination. Inspired by this, we introduce this concept into cooperative multiagent reinforcement learning (MARL), to implicitly address the credit assignment problem and promote the interagent direct interactions for cooperations among agents in cooperative multiagent games. In this article, we propose a novel intrinsic reward method with peer incentives (IRPI) based on actor-critic policy gradient. This method can enable agents to incentivize each other for their cooperations through using causal influence among them. Specifically, a novel intrinsic reward mechanism is innovatively designed to empower each agent the ability to give positive or negative rewards to other peer agents' actions through considering the causal influence of the other agents on it. The mechanism is realized by a feedforward neural network through utilizing causal influence between the agents. The causal influence of one agent on another is inferred via counterfactual reasoning using the joint action-value function in MARL. The quality of the influence is assessed via counterfactual reasoning using the individual value function in MARL. Simulations are carried out on two popular multiagent game testbeds: Starcraft II Micromanagement and Multiagent Particle Environments. Simulation results demonstrate that the proposed IRPI can enhance cooperations among the agents to achieve better performance compared with a number of state-of-the-art MARL methods in a variety of cooperative multiagent games. |
| WOS关键词 | STARCRAFT ; LEVEL |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001128375200007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54904]  |
| 专题 | 复杂系统认知与决策实验室 |
| 通讯作者 | Liu, Zhen |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Tianle,Liu, Zhen,Pu, Zhiqiang,et al. Peer Incentive Reinforcement Learning for Cooperative Multiagent Games[J]. IEEE TRANSACTIONS ON GAMES,2023,15(4):623-636. |
| APA | Zhang, Tianle,Liu, Zhen,Pu, Zhiqiang,&Yi, Jianqiang.(2023).Peer Incentive Reinforcement Learning for Cooperative Multiagent Games.IEEE TRANSACTIONS ON GAMES,15(4),623-636. |
| MLA | Zhang, Tianle,et al."Peer Incentive Reinforcement Learning for Cooperative Multiagent Games".IEEE TRANSACTIONS ON GAMES 15.4(2023):623-636. |
入库方式： OAI收割
来源：自动化研究所
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