Q-learning in the Reinforcement Learning (RL) field is the powerful and attractive tool to make robots generate autonomous behavior. But it needs large amount of computational cost because of its discrete state and action. To generated smooth trajectory with less computational cost, we propose two ingredients for Q-learning. We applied Q-learning to the simulated two wheeled robot to generate trajectory for Ball-To-Goal task in robot soccer.
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