Learning Simple Algorithms from Examples
This addresses the challenge of algorithm learning from data for AI systems, but it is incremental as it builds on existing neural network and reinforcement learning methods.
The paper tackles the problem of learning simple algorithms like copying, multi-digit addition, and single-digit multiplication directly from examples, achieving generalization to test examples with thousands of digits.
We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using $Q$-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by $Q$-learning.