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We propose a new reinforcement learning algorithm for partially observableMarkov decision processes (POMDP) based on spectral decomposition methods.While spectral methods have been previously employed for consistent learning of(passive) latent variable models such as hidden Markov models, POMDPs are morechallenging since the learner interacts with the environment and possiblychanges the future observations in the process. We devise a learning algorithmrunning through epochs, in each epoch we employ spectral techniques to learnthe POMDP parameters from a trajectory generated by a fixed policy. At the endof the epoch, an optimization oracle returns the optimal memoryless planningpolicy which maximizes the expected reward based on the estimated POMDP model.We prove an order-optimal regret bound with respect to the optimal memorylesspolicy and efficient scaling with respect to the dimensionality of observationand action spaces.

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods