Harnessing the flexibility of neural networks to predict meaningful theoretical parameters in a multi-armed bandit task
Yoav Ger, Eliya Nachmani, Lior Wolf, Nitzan Shahar · COSYNE 2023
Classical studies in reinforcement learning (RL) heavily rely on normative models of behavior, models that often stress interpretability over predictive capabilities. More recently, neural network models emerged as a descriptive modeling paradigm capable of high predictive power, however, with limited interpretation. To address these issues, we sought to augment the expressiveness of theoretically interpretable RL models with the high flexibility and predictive power of neural networks. Specifically, we introduce a novel framework whereby a neural network is trained to predict theoretically me
Sun, Mar 12, 2023