Differential reinforcement-type shaping Q-Learning method based on animal training for autonomous mobile robot

The general idea of ldquoshapingrdquo used by ethology, behavior analysis or animal training is a remarkable method. ldquoShapingrdquo is a general idea that the learner is given a reinforcement signal step by step gradually and inductively forward the behavior from easy tasks to complicated tasks. In this paper, we propose a shaping reinforcement learning method took in a general idea of shaping to the reinforcement learning that can acquire a desired behavior by the repeated search autonomously. Three different shaping reinforcement learning methods used Q-learning, profit sharing, and actor-critic to check the efficiency of the shaping were proposed at first. Furthermore, we proposed the differential reinforcement-type shaping Q-learning (DR-SQL) applied a general idea of ldquodifferential reinforcementrdquo to reinforce a special behavior step by step such as real animal training, and confirmed the effectiveness of these methods by the simulation experiment of grid search problem.