1. ICCM
    First page of the paper: Biologically-Plausible Memory for Continuous Time Reinforcement Learning

    Biologically-Plausible Memory for Continuous Time Reinforcement Learning

    Madeleine Bartlett*, Nicole Sandra-Yaffa Dumont*, Michael P. Furlong, and Terry C. Stewart
    In International Conference on Cognitive Modelling (ICCM) 2022, Jul 2022
    PDF Code

    Abstract

    Reinforcement learning, and particularly Temporal Difference learning, has been inspired by, and offers insights into, the mechanisms underlying animal learning. An ongoing challenge to providing biologically realistic models of learning is the need for algorithms that operate in continuous time and can be implemented with spiking neural networks. This paper presents a novel approach to Temporal Difference learning in continuous time – TD(θ). This approach relies on the use of Legendre Delay Networks for storing information about the past that will be used to update the value function. A comparison of the discrete-time TD(n) and continuous TD(θ) rules on a simple spatial navigation RL task in a largely non-spiking network is presented, and the theoretical implications and avenues for future work are discussed.

    BibTeX

    @conference{bartlett2022,
  author = {Bartlett, Madeleine and Dumont, Nicole Sandra-Yaffa and Furlong, Michael P. and Stewart, Terry C.},
  title = {Biologically-Plausible Memory for Continuous Time Reinforcement Learning},
  booktitle = {International Conference on Cognitive Modelling ({ICCM}) 2022},
  year = {2022},
  address = {Toronto, Canada},
  month = jul,
}