Autonomous control of a snake-like robot using reinforcement learning -Discussion of the role of the mechanical body in abstraction of state-action space-

In this paper we consider autonomous control of a snake-like robot using reinforcement learning. Conventional methods of reinforcement learning have significant problems in practical use. That is curse of dimensionally and lack of generality. To solve these problems, we focus on design of the mechanical body of the snake-like robot, and abstract necessary small state-action space from complex environments by utilizing the function of the body. To discuss the function of the body, experiments have been conducted and transition probability has been identified. As the result, we confirmed that by the function of the body, learning machine can observe different complex environments as similar simple environments.