Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping

We present a new algorithm, Prioritized Sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as Temporal Differencing and Q-Iearning have fast real time performance. Classicalmethods are slower, but more accurate, because they make full use of the observations. Prioritized Sweeping aims for the best of both worlds. It uses all previous experiences both to prioritize important dynamicprogramming sweeps and to guide the exploration of statespace.