Human Strategy Adaptation in Reinforcement Learning Resembles Policy Gradient Ascent.

ABSTRACT: A hallmark of intelligence is the ability to adapt behavior to changing environments, which requires adapting one's own learning strategies. This phenomenon is known as learning to learn in cognitive science and meta-learning in artificial intelligence. While this phenomenon is well-established in humans and animals, no quantitative framework exists for characterizing the trajectories through which biological agents adapt their learning strategies. Previous computational studies that either assume fixed strategies or use task-optimized neural networks do not explain how humans refine strategies through experience. Here we show that humans adjust their reinforcement learning strategies resembling principles of gradient-based online optimization. We introduce DynamicRL, a framework using neu

SUBMITTER: Xiong HD 

PROVIDER: S-EPMC12324363 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature