Model-Based Reinforcement Learning in Robotics: A Survey

The model-based reinforcement learning makes robots closer to human-like learning and interaction by learning an environment model and optimizing policy or planning based on the model. In this paper, the definition of robot learning problems is described, and model-based reinforcement learning methods in robot learning are introduced, including mainstream model learning and model utilization methods. The mainstream model learning methods are given including the forward dynamics model, the inverse dynamics model and the implicit model. The model utilization methods are presented including model-based planning, model-based policy learning and implicit planning. The current problems on model-based reinforcement learning are discussed. Aiming at the problems of the robot learning task in reality, the application of model-based reinforcement learning is illustrated and the future research directions are analyzed. © 2022, Science Press. All right reserved.