Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning

Understand

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 the entire tree that improves sample complexity affords interpretable policy extraction.
• First, we include theoretical motivation on the need for policy-gradient learning by examining the properties of gradient descent over differentiable decision trees.