Deep Learning for Puzzles and Circadian Rhythms
Abstract
The combination of deep learning with reinforcement learning and the application of deep learning to the sciences is a relatively new and flourishing field. We show how deep reinforcement learning techniques can learn to solve problems, often in the most efficient way possible, when faced with many possibilities but little information by designing an algorithm that can learn to solve seven different combinatorial puzzles, including the Rubik's cube. Furthermore, we show how deep learning can be applied to the field of circadian rhythms. Circadian rhythms are fundamental for all forms of life. Using deep learning, we can gain insight into circadian rhythms on the molecular level. Finally, we propose new deep learning algorithms that yield significant performance improvements on computer vision and high energy physics tasks.