Modelling Sparse Dynamical Systems with Compressed Predictive State Representations

Efficiently learning accurate models of dynamical systems is of central importance for developing rational agents that can succeed in a wide range of challenging domains. The difficulty of this learning problem is particularly acute in settings with large observation spaces and partial observability. We present a new algorithm, called Compressed Predictive State Representation (CPSR), for learning models of high-dimensional partially observable uncontrolled dynamical systems from small sample sets. The algorithm exploits a particular sparse structure present in many domains. This sparse structure is used to compress information during learning, allowing for an increase in both the efficiency and predictive power. The compression technique also relieves the burden of domain specific feature selection. We present empirical results showing that the algorithm is able to build accurate models more efficiently than its uncompressed counterparts, and we provide theoretical results on the accuracy of the learned compressed model.