Q Learning Parallels to Behavior: The Quest for an Accurate State Representation

<b>Abstract: </b>This paper is directed by the question “To what extent does Q-learning imitate reinforcement learning in rats?” Using computational modeling with empirical validation of an externally sourced rat data set, and cross-validated parameter optimization using Bayesian optimization, this paper recognizes the importance of an accurate state representation when training a model. The task at hand on which the Q learning model is trained and evaluated is a double T maze with reversing goals, created by the Blair Lab at UCLA. The experimental process this paper follows is first, building the digital environment of the model, training it on a Q Learning model, and optimizing hyperparameters of the Q learning model with Optuna’s Bayesian optimizer. The resulting observations highlight the importance of choosing an optimal state representation to improve the interpretation of the learning mechanisms underlying biological experiments.