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.
Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations
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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.