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We propose a novel training algorithm for reinforcement learning whichcombines the strength of deep Q-learning with a constrained optimizationapproach to tighten optimality and encourage faster reward propagation. Ournovel technique makes deep reinforcement learning more practical by drasticallyreducing the training time. We evaluate the performance of our approach on the49 games of the challenging Arcade Learning Environment, and report significantimprovements in both training time and accuracy.

Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening