Model-based reinforcement learning with model error and its application

This paper proposes a Reinforcement Learning (RL) algorithm called Model Error based Forward Planning Reinforcement Learning (ME-FPRL). In this algorithm, an agent controls the amount of learning by using the model error. This study applies ME-FPRL to the pursuit of a target by a robot camera. The results of this application show that ME-FPRL is more efficient than Usual RL and Model-based RL.