Q-functions of infinite-horizon discounted MDPs with finite action sets are approximable by leaky ReLU networks with polynomially growing parameter counts, provided rewards and transitions are themselves DNN-approximable.
Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equa- tions
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Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality
Q-functions of infinite-horizon discounted MDPs with finite action sets are approximable by leaky ReLU networks with polynomially growing parameter counts, provided rewards and transitions are themselves DNN-approximable.