Robot Control via SGA-based Reinforcement Learning Algorithms

SGA 기반 강화학습 알고리즘을 이용한 로봇 제어

The SGA(stochastic gradient ascent) algorithm is one of the most important tools in the area of reinforcement learning, and has been applied to a wide range of practical problems. In particular, this learning method was successfully applied by Kimura et a1. [1] to the control of a simple creeping robot which has finite number of control input choices. In this paper, we considered the application of the SGA algorithm to Kimura's robot control problem for the case that the control input is not confined to a finite set but can be chosen from a infinite subset of the real numbers. We also developed a MATLAB-based robot animation program, which showed the effectiveness of the training algorithms vividly.