A survey of reinforcement learning research and its application for multi-robot systems

Reinforcement learning aims to obtain optimal/suboptimal strategy through trial-and-error and interaction with dynamic environment. After an introduction of basic knowledge of reinforcement learning, TD algorithm, Q-learning algorithm, Dyna algorithm and Sarsa algorithm base on Markov decision model are discussed, respectively. Moreover, reinforcement learning based on partially observable Markov decision process and semi-Markov decision model for uncertain environment are analyzed, respectively. The research status of Q learning in the field of multi-robot systems is also presented. Finally, the main challenges and further research work are given.