Neural Programmer-Interpreters

TL;DR

The paper introduces the neural programmer-interpreter (NPI), a neural network architecture that learns to represent, compose, and execute programs across diverse environments, improving generalization and sample efficiency.

Contribution

The NPI architecture combines a recurrent core, program memory, and domain-specific encoders to enable multi-environment program learning and compositionality, advancing neural program induction.

Findings

NPI successfully learns addition, sorting, and 3D model canonicalization.

A single NPI can execute 21 subprograms and their compositions.

NPI demonstrates improved generalization and sample efficiency.

Abstract

We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances. By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-to-sequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs. NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input. Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.