Reinforcement learning neural Turing machines
Venue
Google Inc. (2015)
Publication Year
2015
Authors
Wojciech Zaremba, Ilya Sutskever
BibTeX
Abstract
The Neural Turing Machine (NTM) is more expressive than all previously considered
models because of its external memory. It can be viewed as a broader effort to use
abstract external Interfaces and to learn a parametric model that interacts with
them. The capabilities of a model can be extended by providing it with proper
Interfaces that interact with the world. These external Interfaces include memory,
a database, a search engine, or a piece of software such as a theorem verifier.
Some of these Interfaces are provided by the developers of the model. However, many
important existing Interfaces, such as databases and search engines, are discrete.
We examine feasibility of learning models to interact with discrete Interfaces. We
investigate the following discrete Interfaces: a memory Tape, an input Tape, and an
output Tape. We use a Reinforcement Learning algorithm to train a neural network
that interacts with such Interfaces to solve simple algorithmic tasks. Our
Interfaces are expressive enough to make our model Turing complete.