Reinforcement learning for mobile robot: From reaction to deliberation
Chunlin Chen, Zonghai Chen · Journal of Systems Engineering and Electronics · 2012
Reinforcement learning has been widely used for mobile robot learning and control. Some progress of this kind of ap2 proaches is surveyed and argued in a new way which emphasizes on different levels of algorithms according to different complexity of tasks. The central conjecture is that approaches which combine reactive and deliberative control to robotics scale better to com2 plex real2world applications than purely reactive or deliberative ones. This paper describes basic reactive reinforcement learning al2 gorithms and two classes of approaches to achieve deliberation , which are modular methods and hierarchical methods. By combin2 ing reactive and deliberative paradigms , the whole system gains advantages from different control levels. The paper gives results of experiments as a case study to verify the effectiveness of the proposed approaches.