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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nakahara, M. Osana, Y. |
| Copyright Year | 2009 |
| Description | Author affiliation: Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology, Hachioji, Tokyo (Nakahara, M.) || School of Computer Science, Tokyo University of Technology, Hachioji, Tokyo (Osana, Y.) |
| Abstract | In this paper, we propose a method for generating roles by redistribution of reward between agents in reinforcement learning of multiagent system. In the proposed method, there are two kinds of reward; (1) reward obtained from environment when agents reach the goal and (2) reward received from other agents. Agents can learn actions to work cooperatively with other agents by the reward received from other agents, and the roles of agents are generated. In the proposed method, agents can decide how much reward to which agent to give from the obtained reward by using the history that records actions at the time when the change of environment are recognized. We carried out computer experiments for two tasks; (1) path finding problem and (2) transportation problem, and confirmed that roles of agents are generated by redistribution of reward between agents in the proposed method. Moreover, we confirmed that the proposed method can learn as similar as when the designer decides conditions for redistribution of reward. |
| Starting Page | 2259 |
| Ending Page | 2264 |
| File Size | 182845 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424427932 |
| ISSN | 1062922X |
| DOI | 10.1109/ICSMC.2009.5345952 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-10-11 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning Multiagent systems History Cybernetics USA Councils Computer science Road transportation Temperature Cooperative systems Reinforcement Learning Generation of Roles Redistribution of Reward Multiagent System |
| Content Type | Text |
| Resource Type | Article |
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