Why Humans Are Not Reinforcement Learning Agents — And Why This Matters for AI
Last Updated on December 29, 2025 by Editorial Team
Author(s): Shenggang Li
Originally published on Towards AI.
Reward instability, shifting perspectives, and the hidden limits of classical reinforcement learning
Modern AI systems rely heavily on attention. It allows models to focus, reason over context, and scale to massive inputs.
The article examines the limitations of classical reinforcement learning when applied to human decision-making, highlighting that human actions are influenced by changing rewards and contexts, emotional states, and the active construction of decision states, making them fundamentally different from the predictable behaviors modeled by traditional reinforcement learning frameworks. The implications for AI system design and understanding human behavior are critically discussed, suggesting a need for models that can accommodate the complexities of human decision-making rather than conforming strictly to reinforcement learning paradigms.
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