Hybrid Q-learning algorithm about cooperation in MAS
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Retrieving and reusing game plays for robot soccer
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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Multi-agent Systems have emerged as an active sub field of Artificial Intelligence. Machine learning techniques have played a significant role by handling the inherent complexity of such systems. Robotic Soccer is a typical multi-agent system, wherein the challenge is to develop and hone the skills of the agents that take part in the game. For an indepth and sophisticated understanding of the game, soccerplaying agents must possess the capability to learn and acquire low-level skills. These skills can later be put together and used to emulate the expertise of experienced players. This paper describes the use of reinforcement learning, a machine learning technique, to acquire the base level skills of intercepting a moving ball. Results of simulation runs using the Robocup Soccer server have also been presented.