Model-Based Reinforcement Learning

Learnig on-line can be very expensive in the real world. We propose to learn the model of the environment while obtaining on-line experience and then use this model to facilitate learning and avoid costly actual experiences. The model can be learned by diierent value approximation schemes. In this paper the results are shown for the most straight forward implementation. Nevertheless the improvement in the convergence speed is signiicant.