Modular Learning Systems for Soccer Robot
نویسنده
چکیده
This paper presents a series of the studies of modular learning system for vision-based behavior acquisition of a soccer robot participating in middle size league of RoboCup (Asada, et al. 1999). Reinforcement learning has recently been receiving increased attention as a method for behavior learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. However, simple and straightforward application of reinforcement learning methods to real robot tasks is considerably difficult due to its endless exploration of which time is easily scaled up exponentially with the size of the state/action spaces, that seems almost impossible from a practical viewpoint. Further, the existing reinforcement learning approaches have been suffering from the policy alternation of others in multi-agent dynamic environments such as RoboCup competitions since other agent behaviors may cause sudden changes of state transition probabilities of which constancy is necessary for the learning to converge. In order to cope with the above two issues, we introduced a multi-layered modular learning system. To show the validity of the proposed methods, we apply them to simple soccer situations in the context of RoboCup with real robots, and show the experimental results.
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تاریخ انتشار 2004