基于模型的机器人强化学习研究综述

Model-Based Reinforcement Learning in Robotics:A Survey

基于模型的强化学习通过学习一个环境模型和基于此模型的策略优化或规划,实现机器人更接近于人类的学习和交互方式.文中简述机器人学习问题的定义,介绍机器人学习中基于模型的强化学习方法,包括主流的模型学习及模型利用的方法.主流的模型学习方法具体介绍前向动力学模型、逆向动力学模型和隐式模型.模型利用的方法具体介绍基于模型的规划、基于模型的策略学习和隐式规划,并对其中存在的问题进行探讨.最后,结合现实中机器人学习任务面临的问题,介绍基于模型的强化学习在其中的应用,并展望未来的研究方向.

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.