Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture

TL;DR

This paper presents REPRISE, a neural inference scheme that learns to infer unobservable events and predict future sensorimotor states, enabling goal-directed control through active inference in dynamical systems.

Contribution

It introduces a novel neural architecture that combines retrospective and prospective inference for learning and controlling complex sensorimotor dynamics.

Findings

REPRISE effectively separates and models sensorimotor dynamics.

The system enables goal-directed control via model-predictive active inference.

Event encodings facilitate adaptive sensorimotor behavior.

Abstract

We introduce REPRISE, a REtrospective and PRospective Inference SchEme, which learns temporal event-predictive models of dynamical systems. REPRISE infers the unobservable contextual event state and accompanying temporal predictive models that best explain the recently encountered sensorimotor experiences retrospectively. Meanwhile, it optimizes upcoming motor activities prospectively in a goal-directed manner. Here, REPRISE is implemented by a recurrent neural network (RNN), which learns temporal forward models of the sensorimotor contingencies generated by different simulated dynamic vehicles. The RNN is augmented with contextual neurons, which enable the encoding of distinct, but related, sensorimotor dynamics as compact event codes. We show that REPRISE concurrently learns to separate and approximate the encountered sensorimotor dynamics: it analyzes sensorimotor error signals adapting both internal contextual neural activities and connection weight values. Moreover, we show that REPRISE can exploit the learned model to induce goal-directed, model-predictive control, that is, approximate active inference: Given a goal state, the system imagines a motor command sequence optimizing it with the prospective objective to minimize the distance to the goal. The RNN activities thus continuously imagine the upcoming future and reflect on the recent past, optimizing the predictive model, the hidden neural state activities, and the upcoming motor activities. As a result, event-predictive neural encodings develop, which allow the invocation of highly effective and adaptive goal-directed sensorimotor control.