Reverse Experience Replay

19 Oct 2019 Egor Rotinov

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... (read more)

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Methods used in the Paper

Double Q-learning
Off-Policy TD Control
Double DQN
Q-Learning Networks
Dense Connections
Feedforward Networks
Q-Learning Networks
Experience Replay
Replay Memory
Off-Policy TD Control