This paper addresses the problem of learning the optimal feedback policy for a nonlinear stochastic dynamical system. Feedback policies typically need a high dimensional parametrization, which makes Reinforcement Learning (RL) algorithms that search for an optimum in this large parameter space, sample inefficient and subject to high variance... (read more)
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