Accession Number:



Estimating Scale-Invariant Future in Continuous Time

Descriptive Note:

Journal Article - Open Access

Corporate Author:

University of Virginia Charlottesville United States

Report Date:


Pagination or Media Count:



Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next model-based algorithms or a scalar value of exponentially-discounted future reward using the Bellman equation model-free algorithms. An important drawback of model-based algorithms is that computational cost grows linearly with the amount of time to be simulated. On the other hand, an important drawback of model-free algorithms is the need to select a time-scale required for exponential discounting. We present a computational mechanism, developed based on work in psychology and neuroscience, for computing a scale-invariant timeline of future outcomes. This mechanism efficiently computes an estimate of inputs as a function of future time on a logarithmically-compressed scale, and can be used to generate a scale-invariant power-law-discounted estimate of expected future reward. The representation of future time retains information about what will happen when. The entire timeline can be constructed in a single parallel operation which generates concrete behavioral and neural predictions. This computational mechanism could be incorporated into future reinforcement learning algorithms.1 Introduction

Subject Categories:

  • Psychology
  • Human Factors Engineering and Man Machine Systems

Distribution Statement: