抽象化副報酬の自動生成による実ロボット強化学習の高速化

抄録

To acquire a real robot behaviors, it is important to learn in the real environment. Although most reinforcement learning researches have been made learning by simulation since the real environment learning takes large computation costs and also needs a lot of time. To realize the reinforcement learning of a real robot, it requires both an adaptation for the diversity of situations and a high speed learning method that can even learn from less trials. This paper describes the Realtime Reinforcement Learning for a Real Robot based on the exploitation oriented reinforcement learning method that it's learning cost is too small and has strictly incrementality to realize Realtime Reinforcement Learning with Automated Sub-Rewards Generation method by the abstraction of the task state to accelerate the learning process. Then the successive learning experiment in the real environment for the ball pushing task for the real robot is performed.