Gradient Boosting Reinforcement Learning
Benjamin Fuhrer, Chen Tessler, Gal Dalal
2024-07-13
Summary
This paper introduces Gradient-Boosting Reinforcement Learning (GBRL), a new framework that combines the strengths of Gradient Boosting Trees (GBT) with reinforcement learning (RL). It aims to improve how AI models learn and make decisions in various tasks, especially when dealing with structured data.
What's the problem?
Neural networks are powerful but have some drawbacks. They can be hard to interpret, may not handle certain types of data well (like categorical features), and can require a lot of resources, making them unsuitable for smaller devices. While Gradient Boosting Trees are good at addressing these issues, they haven't been widely used in reinforcement learning scenarios where models learn from interactions over time.
What's the solution?
GBRL addresses this gap by applying the principles of GBT to reinforcement learning. The authors developed a framework that uses GBTs to improve the efficiency and effectiveness of RL algorithms. They introduced a method called tree-sharing, which allows different parts of the model to share information while learning at different rates. This approach helps the model learn faster and more effectively across many interactions. The paper also presents a high-performance version of GBRL that works well with popular RL libraries.
Why it matters?
This research is significant because it shows how combining GBTs with reinforcement learning can enhance AI's ability to learn from experience, especially in areas where data is structured or categorical. By improving the tools available for RL practitioners, GBRL can lead to better performance in real-world applications like robotics, gaming, and decision-making systems.
Abstract
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.