Acquiring behavior of robot to avoid danger using probability based reinforcement learning

Abstract

In recent years, robots have been active in a dangerous environment such as space and the disaster areas. However, there is a possibility that risk aversion instruction is not in time when a robot became the dangerous scene in such an environment. The robots therefore require acquisition of behavior to avoid danger autonomously. In this paper, we propose a method for avoiding danger using the probability based reinforcement learning (PrRL) to apply to the action acquisition of the robot.