Nihat Ay, Guido ́far, Johannes Rauh

Paper #: 11-09-040

We present ways of defining neuromanifolds – models of stochastic matrices – that are compatible with the maximization of an objective function such as the expected reward in reinforcement learning theory. Our approach is based on information geometry and aims at the reduction of model parameters with the hope to improve gradient learning processes.