Connectionist Reinforcement Learning for Mobile Robot.

By adopting control method based on behavior, mobile robot can accomplish its given task using reinforcement learning while need not know the exact environment mode. The robot needs to memorize the behaviors and states, at the same time the EMS memory space is not enough, the connectionist reinforcement learning can approximate the Q function with MLPs, and generalize the state space to economize the memory space. The results of simulation show that the algorithm is valid, and it resolves the shortcoming that the reinforcement learning with look up table is not fit for the continuous state space. This method provides the bases for the reality of mobile robot. [ABSTRACT FROM AUTHOR]