At the heart of the Q-learning method is a function, which is called the Q-function. Modeling this function is what takes most of the memory resources used by this method. Several methods have been devised to tackle the Q-learning’s shortcomings, with relatively good success. However, even the most promising methods do a poor job at distributing the memory resources available to model the Q-function, which, in turn, limits the number of problems that can be solved by Q-learning. A new method called Moving Prototypes is proposed to alleviate this problem.
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