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We propose an optimistic model-based algorithm, dubbed SMRL, forfinite-horizon episodic reinforcement learning (RL) when the transition modelis specified by exponential family distributions with $d$ parameters and thereward is bounded and known. SMRL uses score matching, an unnormalized densityestimation technique that enables efficient estimation of the model parameterby ridge regression. Under standard regularity assumptions, SMRL achieves$\tilde O(d\sqrt{H^3T})$ online regret, where $H$ is the length of each episodeand $T$ is the total number of interactions (ignoring polynomial dependence onstructural scale parameters).

Exponential Family Model-Based Reinforcement Learning via Score Matching