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Temporal-difference (TD) networks are a class of predictive staterepresentations that use well-established TD methods to learn models ofpartially observable dynamical systems. Previous research with TD networks hasdealt only with dynamical systems with finite sets of observations and actions.We present an algorithm for learning TD network representations of dynamicalsystems with continuous observations and actions. Our results show that thealgorithm is capable of learning accurate and robust models of several noisycontinuous dynamical systems. The algorithm presented here is the first fullyincremental method for learning a predictive representation of a continuousdynamical system.

Temporal-Difference Networks for Dynamical Systems with Continuous Observations and Actions