
보상 학습 과정에서 자동화된 전략과 의도적인 전략 간의 시간에 따른 변화를 보여준다.


coremsg

Different brain systems have been hypothesized to subserve multiple “experts” that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying “hidden” states that capture shifts in agent contributions over time. Applying this model to a multi-step,reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

### Competing Interest Statement

The authors have declared no competing interest.

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

보상-학습-과정에서-시간에-따른-전략-변화를-보여주는-동적-강화-학습


보상 학습 과정에서 시간에 따른 전략 변화를 보여주는 동적 강화 학습
