Simulated Q-Learning in Preparation for a Real Robot in a Real Environment
Lavi M. Zamstein, A. Antonio Arroyo · 2009
are many cases where it is not possible to program a robot with precise instructions. The environment may be unknown, or the programmer may not even know the best way in which to solve a problem. In cases such as these, intelligent machine learning is useful in order to provide the robot, or agent, with a policy, a set schema for determining choices based on inputs. Reinforcement Learning, and specifically Q-Learning, can be used to allow the agent to teach itself an optimal policy. Because of the large number of iterations required for Q-Learning to reach an optimal policy, a simulator was required. This simulator provided a means by which the agent could learn behaviors without the need to worry about such things as parts wearing down or an untrained robot colliding with a wall. KeywordsLearning, Q-Learning, Simulator