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Reinforcement learning has been widely used for mobile robot learning and control. Some progress of this kind of approaches 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 complex real-world applications than purely reactive or deliberative ones. This paper describes basic reactive reinforcement learning algorithms and two classes of approaches to achieve deliberation, which are modular methods and hierarchical methods. By combining 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.