Cited by 16Reinforcement Learning in RoboticsPsychiatry, Mental Health, NeuroscienceEmbodied and Extended Cognition

From reinforcement learning to agency: Frameworks for understanding basal cognition

Open access
Seifert, Gabriella, Sealander, Ava, Marzen, Sarah, Levin, Michael·2024·BioSystems

Abstract

Organisms play, explore, and mimic those around them. Is there a purpose to this behavior? Are organisms just behaving, or are they trying to achieve goals? We believe this is a false dichotomy. To that end, to understand organisms, we attempt to unify two approaches for understanding complex agents, whether evolved or engineered. We argue that formalisms describing multiscale competencies and goal-directedness in biology (e.g., TAME), and reinforcement learning (RL), can be combined in a symbiotic framework. While RL has been largely focused on higher-level organisms and robots of high complexity, TAME is naturally capable of describing lower-level organisms and minimal agents as well. We propose several novel questions that come from using RL/TAME to understand biology as well as ones that come from using biology to formulate new theory in AI. We hope that the research programs proposed in this piece shape future efforts to understand biological organisms and also future efforts to build artificial agents.

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Crossref · 10.1016/j.biosystems.2023.105107

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  • In applications where action or intelligence is needed, such as for safety or efficiency management through automation with utilities, robotics or autonomous vehicles or processes and systems, agentic AI systems use action-based datasets to learn how to recognize anomalies, mitigate threats, or identify the best actions to optimize processes.

    “In applications where action or intelligence is needed, such as for safety or efficiency management through automation with utilities, robotics or autonomous vehicles or processes and systems (such as supply chains), agentic AI systems use action-based datasets, which contain information about conducted actions, so they can learn how to recognize anomalies [117], mitigate threats, or identify the”

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Authors
Seifert, G., Sealander, A., Marzen, S., & Levin, M.
Affiliations
  • Claremont McKenna College (US)
  • Tufts University (US)
Corresponding author
Marzen, Sarah
Venue
BioSystems
Publisher
Elsevier BV
Published
2024-01
Volume
235
Pages
105107
ISSN
0303-2647 (ISSN-L)
OpenAlex
W4389956244
Cited by
16
References
108
Citations by year
2026: 6 · 2025: 7 · 2024: 3
FWCI
1.38
Type
journal article
Language
en
Open access
Locations
  • Biosystems (acceptedVersion)
  • PubMed (publishedVersion)
Funders
  • Air Force Office of Scientific Research
  • John Templeton Foundation
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
Keywords
basal cognition, reinforcement learning, goal-directed behavior, agency, living organisms, artificial agents, cognitive evolution, AI theory
MeSH terms
Cognition, Learning, Motivation, Reinforcement, Psychology

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