Proposal of learning method which selects objectives based on the state

摘要

Reinforcement learning (RL) is one of the methods for robot action learning. RL is formulated as the maximization of a single reward; however, in most practical problems, multiple objectives need to be considered. Therefore, it is necessary to perform multi-objective optimization. We focus on the required objectives that depended on the state of the robot and propose a multi-objective optimization for the required objectives. If there is more than one required objective, multi-objective optimization is performed based on the priority of each objective. In this paper, we give two objectives to a robot and perform simulation experiments. We will demonstrate the validity of the proposed system using the simulation results.