  This paper describes an improvement in Deep Q-learning called Reverse
Experience Replay (also RER) that solves the problem of sparse rewards and
helps to deal with reward maximizing tasks by sampling transitions successively
in reverse order. On tasks with enough experience for training and enough
Experience Replay memory capacity, Deep Q-learning Network with Reverse
Experience Replay shows competitive results against both Double DQN, with a
standard Experience Replay, and vanilla DQN. Also, RER achieves significantly
increased results in tasks with a lack of experience and Replay memory
capacity.
