Development of self-learning vision-based mobile robots for acquiring soccer robots behaviors

An input generalization problem is one of the most important ones in applying reinforcement learning to real robot tasks. To cope with this problem, we propose a self-partitioning state space algorithm which can make non-uniform quantization of the state space. To show that our algorithm has generalization capability, we apply our method to two tasks in which a soccer robot shoots a ball into a goal and prevents a ball from entering a goal. To show the validity of this method, the experimental results for computer simulation and a real robot are shown