2003 Fiscal Year Final Research Report Summary
MULTI-AGENT REINFORCEMENT LEARNING WITH NEUROEVOLUTION
| Project/Area Number |
14580421
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokushima |
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Principal Investigator |
ONO Norihiko THE UNIVERSITY OF TOKUSHIMA, FACULTY OF ENGINEERING, PROFESSOR, 工学部, 教授 (60194594)
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| Co-Investigator(Kenkyū-buntansha) |
ONO Isao THE UNIVERSITY OF TOKUSHIMA, FACULTY OF ENGINEERING, ASSOCIATE PROFESSOR, 工学部, 助教授 (00304551)
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| Project Period (FY) |
2002 – 2003
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| Keywords | MULTI-AGENT SYSTEMS / MULTI-AGENT LEARNING / REINFORCEMENT LEARNING / MACHINE LEARNING / EVOLUTIONARY COMPUTATION / NEURAL NETWORKS / NEURO-EVOLUTION / REAL-CODED GA |
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| Research Abstract |
Several attempts have been reported to let multiple monolithic reinforcement learning (RL) agents synthesize highly coordinated behavior needed to accomplish their common goal effectively. Most of these straightforward application of RL scale poorly to more complex multi-agent learning problems, because the state space for each RL agent grows exponentially with the number of its partner agents engaged in the joint task.
To cope with the exponentially large state space in multi-agent RL (MARL), we previously proposed a MARL scheme, based on neural network representation of the decision policy for an agent and its optimization with a real-coded GA, and showed the effectiveness of the scheme through its application to those multi-agent learning problems that can not be solved appropriately using any other conventional MARL scheme, such as the asynchronous multi-agent seesaw balancing problem and the dynamic channel allocation problem in cellular telephone systems.
However, we can not apply the scheme directly to the design problems of large-scale multi-agent systems (MASs), because the scheme needs a huge amount of computation resources. To remedy the drawback, we propose a hierarchical design scheme of a large-scale MAS, which simply decomposes the whole task of the MAS into its subtasks hierarchically and optimizes each of the subtasks with the above-mentioned MARL scheme. The effectiveness of the design scheme is shown through its application to the RoboCup soccer team design problem where the task of the team is decomposed into the primitive actions by the soccer agents, interaction among the actions and coordination by the agents.
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Research Products
(22 results)
