Taylor Killian

Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning

Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot be updated online via stochastic gradient descent. We overcome this limitation by allowing for a gradient update over th…