A Study on Skill Acquisition in Trailer-Truck Steering Problem by Reinforcement Learning

Abstract

This paper presents an attempt to apply reinforcement learning to a trailer-truck steering problem as one of the skill acquisition problems. Because the learning agent in this problem needs to learn long sequences of actions to reach the goal, it is necessary for the agent to acquire proficient skills for steering. We construct the simulation environment for the problem and try to acquire the steering operations by reinforcement learning. Furthermore, two kinds of action selection methods, Boltzmann selection and ε-greedy selection, are examined to reveal the difference between them through the simulation experiments.