Characterizing a Brain-Based Value-Function Approximator

Abstract

The field of Reinforcement Learning (RL) in machine learning relates significantly to the domains of classical and instrumental conditioning in psychology, which give an understanding of biology’s approach to RL. In recent years, there has been a thrust to correlate some machine learning RL algorithms with brain structure and function, a benefit to both fields. Our focus has been on one such structure, the striatum, from which we have built a general model. In machine learning terms, this model is equivalent to a value-function approximator (VFA) that learns according to Temporal Difference error. In keeping with a biological approach to RL, the present work seeks to evaluate the robustness of this striatum-based VFA using biological criteria. We selected five classical conditioning tests to expose the learning accuracy and efficiency of the VFA for simple state-value associations. Manually setting the VFA’s many parameters to reasonable values, we characterize it by varying each parameter independently and repeatedly running the tests. The results show that this VFA is both capable of performing the selected tests and is quite robust to changes in parameters. Test results also reveal aspects of how this VFA encodes reward value.