Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light

• URL: http://arxiv.org/abs/2507.11482v3
• Date: Mon, 28 Jul 2025 18:54:04 GMT
• Title: Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
• Authors: Mani Hamidi, Terrence W. Deacon,
• Abstract summary: Three core tenets of reinforcement learning have been highlighted as key targets for conceptual revision.<n>We propose a framework inspired by open-ended evolutionary theory to reconsider these three "dogmas"<n>We first establish that evolutionary dynamics can plausibly operate within living brains over an individual's lifetime.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Three core tenets of reinforcement learning (RL)--concerning the definition of agency, the objective of learning, and the scope of the reward hypothesis--have been highlighted as key targets for conceptual revision, with major implications for theory and application. We propose a framework, inspired by open-ended evolutionary theory, to reconsider these three "dogmas." We revisit each assumption and address related concerns raised alongside them. To make our arguments relevant to RL as a model of biological learning, we first establish that evolutionary dynamics can plausibly operate within living brains over an individual's lifetime, and are not confined to cross-generational processes. We begin by revisiting the second dogma, drawing on evolutionary insights to enrich the "adaptation-rather-than-search" view of learning. We then address the third dogma regarding the limits of the reward hypothesis, using analogies from evolutionary fitness to illuminate the scalar reward vs. multi-objective debate. After discussing practical implications for exploration in RL, we turn to the first--and arguably most fundamental--issue: the absence of a formal account of agency. We argue that unlike the other two problems, the evolutionary paradigm alone cannot resolve the agency question, though it gestures in a productive direction. We advocate integrating ideas from origins-of-life theory, where the thermodynamics of sustenance and replication offer promising foundations for understanding agency and resource-constrained reinforcement learning in biological systems.

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