Modular Q-learning based multi-agent cooperation for robot soccer
✍ Scribed by Kui-Hong Park; Yong-Jae Kim; Jong-Hwan Kim
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 532 KB
- Volume
- 35
- Category
- Article
- ISSN
- 0921-8890
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✦ Synopsis
In a multi-agent system, action selection is important for the cooperation and coordination among agents. As the environment is dynamic and complex, modular Q-learning, which is one of the reinforcement learning schemes, is employed in assigning a proper action to an agent in the multi-agent system. The architecture of modular Q-learning consists of learning modules and a mediator module. The mediator module of the modular Q-learning system selects a proper action for the agent based on the Q-value obtained from each learning module. To obtain better performance, along with the Q-value, the mediator module also considers the state information in the action selection process. A uni-vector field is used for robot navigation. In the robot soccer environment, the effectiveness and applicability of modular Q-learning and the uni-vector field method are verified by real experiments using five micro-robots.