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Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions. In this paper, we propose and systematically study deep neural networks-based algorithms to solve stochastic control problems with delay features. Specifically, we employ neural networks for sequence modeling (e.g., recurrent neural networks such as long short-term memory) to parameterize the policy and optimize the objective function. The proposedarXiv:2101.01385v2 fatcat:7plkmhbbabd2zjii2776vgvwyq