Proposal of learning method which selects objectives based on the state
Hironori Miura, Kentarou Kurashige · 2013
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.