All Publications


  • Bayesian Reinforcement Learning With Limited Cognitive Load. Open mind : discoveries in cognitive science Arumugam, D., Ho, M. K., Goodman, N. D., Van Roy, B. 2024; 8: 395-438

    Abstract

    All biological and artificial agents must act given limits on their ability to acquire and process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent's learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This body of work provides an account of capacity-limited Bayesian reinforcement learning, a unifying normative framework for modeling the effect of processing constraints on learning and action selection. Here, we provide an accessible review of recent algorithms and theoretical results in this setting, paying special attention to how these ideas can be applied to studying questions in the cognitive and behavioral sciences.

    View details for DOI 10.1162/opmi_a_00132

    View details for PubMedID 38665544

  • Value Preserving State-Action Abstractions Abel, D., Umbanhowar, N., Khetarpal, K., Arumugam, D., Precup, D., Littman, M., Chiappa, S., Calandra, R. ADDISON-WESLEY PUBL CO. 2020: 1639–49
  • State Abstraction as Compression in Apprenticeship Learning Abel, D., Arumugam, D., Asadi, K., Yuu Jinnai, Littman, M. L., Wong, L. S., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 3134–42