Realtime reinforcement learning for a real robot in the real environment
Takuro Yamaguchi, M. Masubuchi, Kazutsugu Fujihara, M. Yachida · 2002
For a real robot to acquire behaviors, it is important for it to learn in a real environment. Most reinforcement learning research has been made by simulation because real-environment learning requires large computation costs as well as a lot of time. Realizing reinforcement learning of a physical robot in a real environment requires both an adaptation for the diversity of possible situations and a high-speed learning method that can learn from fewer trials. This paper describes the realtime reinforcement learning for a real robot in the real environment based on the exploitation oriented reinforcement learning method where the learning cost is very small and has strict incrementality to realize realtime reinforcement learning with an automated sub-rewards generation method achieved by the abstraction of the task state to accelerate the learning process. Successive learning experiments in the real environment for the ball pushing task for the real robot are performed.