  We propose a novel training algorithm for reinforcement learning which
combines the strength of deep Q-learning with a constrained optimization
approach to tighten optimality and encourage faster reward propagation. Our
novel technique makes deep reinforcement learning more practical by drastically
reducing the training time. We evaluate the performance of our approach on the
49 games of the challenging Arcade Learning Environment, and report significant
improvements in both training time and accuracy.
