A SURVEY OF REINFORCEMENT LEARNING IN AUTONOMOUS MOBILE ROBOTS

Even though autonomous mobile robots based on behaviour approaches are robust for many tasks and environments, they are not necessarily adaptive for dynamic environments. Reinforcement learning (RL) offers a powerful set of techniques that allow a robot to learn a task without requiring its designer to fully specify how it should be carried out. RL is a novel approach to machine intelligence that combines dynamic programming and supervised learning. RL is widely noticed as a promising method for robot learning because of its following advantages: (1) Behaviors of robot can be acquired only by assigning rewards and punishments; (2) Rewards and punishments can have a delay. Firstly, we analyzed the basic problems in robotic learning, and then introduced the principle and basic algorithms of RL, thirdly discussed several important problems and some solving approaches, in the end, pointed out the future development direction.