Abstract
Generally, it is known that the engineering application simulated from the learning mechanism of animals is useful to make learn behaviors of the autonomous agents or mobile robots efficiently. Above all, a general idea of “shaping” used by ethology, behavior analysis or animal training is a remarkable method recently. “Shaping” 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 research, 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 and the experiment by the simulator of grid search was performed. Furthermore, we proposed the Differential Reinforcement-type Shaping Q-Learning (DR-SQL) applied a general idea of “differential reinforcement” to reinforce a special behavior step by step such as real animal training, and confirmed the effectiveness of this method by the simulation experiment.
