Model-based reinforcement learning with model error and its application
Yoshiyuki Tajima, Takehisa Onisawa · 2007
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.