
The brain employs a mixture of reinforcement learning strategies that dynamically shift over the course of a reward-learning task, transitioning from initial exploration to exploitation and reduced engagement.


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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

dynamic-shifts-in-reinforcement-learning-strategies-during-reward-guided-decision-making


Dynamic Shifts in Reinforcement Learning Strategies During Reward-Guided Decision-Making
