抄録
Q-Learning is a popular non-supervised reinforcement learning (RL) technique that learns an optimal actionvalue function characteristic of a learning problem. Due to the complexity of some problems, the number of training episodes to reach the convergence of the learning problem can increase drastically. In order to fasten the learning speed of an agent on a particular problem, researchers have been exploring interactive reinforcement learning (IRL), i.e. a way to interact with an agent so that it does not learn to solve a problem only by itself. This paper proposes an interactive reinforcement learning to try to fasten the learning speed of an agent. Especially, the combination of an agent asking for advice and getting advice from supervisors was explored. A simple way to experiment this combination is an agent evolving on a maze (a gridworld problem) trying to find its path to a fixed goal point. Experiments shows how an interactive learning agent solve the problem compared to a classical learning agent.
