Teaching Your Computer To Play Super Mario Bros. – A Fork of the Google DeepMind Atari Machine Learning Project

The second issue I noticed was that there seemed to be little connection between the network's confidence in its actions and its actual score. I came across another recent paper on something called Double Q Learning, also courtesy of DeepMind, which substantially improved Google's original results. Double Q Learning counters the tendency for Q networks to become overconfident in their predictions. I changed Google's original Deep Q Network to a Double Deep Q Network, and that helped substantially. Finally, the biggest improvement of all came when I was just more patient. Even running on a powerful machine with a Nvidia 980 GPU, the emulator could only go so fast. As a consequence, one million training steps took about an entire day, with quite a bit of variance in the scores along the way.