Gradient Boosting Reinforcement Learning
Authors: name: Benjamin Fuhrer, name: Chen Tessler, name: Gal Dalal

Neural networks (NN) achieve remarkable results in various tasks, but lack
key characteristics: interpretability, support for categorical features, and
lightweight implementations suitable for edge devices. While ongoing efforts
aim to address these challenges, Gradient Boosting Trees (GBT) inherently meet
these requirements. As a result, GBTs have become the go-to method for
supervised learning tasks in many real-world applications and competitions.
However, their application in online learning scenarios, notably in
reinforcement learning (RL), has been limited. In this work, we bridge this gap
by introducing Gradient-Boosting RL (GBRL), a framework that extends the
advantages of GBT to the RL domain. Using the GBRL framework, we implement
various actor-critic algorithms and compare their performance with their NN
counterparts. Inspired by shared backbones in NN we introduce a tree-sharing
approach for policy and value functions with distinct learning rates, enhancing
learning efficiency over millions of interactions. GBRL achieves competitive
performance across a diverse array of tasks, excelling in domains with
structured or categorical features. Additionally, we present a
high-performance, GPU-accelerated implementation that integrates seamlessly
with widely-used RL libraries (available at https://github.com/NVlabs/gbrl).
GBRL expands the toolkit for RL practitioners, demonstrating the viability and
promise of GBT within the RL paradigm, particularly in domains characterized by
structured or categorical features.