  This paper investigates the problem of impact-time-control and proposes a
learning-based computational guidance algorithm to solve this problem. The
proposed guidance algorithm is developed based on a general
prediction-correction concept: the exact time-to-go under proportional
navigation guidance with realistic aerodynamic characteristics is estimated by
a deep neural network and a biased command to nullify the impact time error is
developed by utilizing the emerging reinforcement learning techniques. The deep
neural network is augmented into the reinforcement learning block to resolve
the issue of sparse reward that has been observed in typical reinforcement
learning formulation. Extensive numerical simulations are conducted to support
the proposed algorithm.
