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.
Overview
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The paper introduces a new framework called Gradient-Boosting Reinforcement Learning (GBRL), which combines Gradient Boosting Trees (GBT) with Reinforcement Learning (RL) to enhance efficiency and interpretability in RL tasks.
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A shared tree-based actor-critic architecture is proposed, where GBT-based actor-critic algorithms like PPO, A2C, and AWR optimize policy and value functions within a single ensemble structure, reducing memory and computational requirements.
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Experiments indicate that GBRL achieves competitive performance with neural networks in RL domains, particularly excelling in environments with structured and categorical data, showcasing its robustness and potential for practical applications in fields such as finance, healthcare, and edge computing.
Gradient Boosting Reinforcement Learning (GBRL)
The paper "Gradient Boosting Reinforcement Learning" authored by Benjamin Fuhrer, Chen Tessler, and Gal Dalal from NVIDIA investigates the integration of Gradient Boosting Trees (GBT) into the realm of Reinforcement Learning (RL). It introduces a new framework, Gradient-Boosting RL (GBRL), aiming to utilize the inherent benefits of GBTs—such as interpretability, efficiency with categorical features, and suitability for deployment on edge devices—in the dynamic and sequential decision-making context of RL.
Main Contributions
- GBRL Framework for RL: This work proposes GBRL, a framework that effectively combines GBT and RL for the first time. The authors implement GBT-based versions of widely used actor-critic (AC) RL algorithms, including PPO, A2C, and AWR, demonstrating that GBTs can serve as effective function approximators in RL. These GBT-based RL algorithms are shown to achieve competitive performance compared to traditional neural networks (NNs) in various RL domains.
- Tree-based Actor-Critic Architecture: The paper introduces a shared tree-based actor-critic architecture that optimizes the policy and value functions concurrently within a single ensemble structure. This architecture is designed to reduce memory and computational requirements by sharing the tree structure between the actor and critic, which mitigates the high computational demand typically associated with GBTs in large-scale RL tasks.
- Modern GBT-based RL Library: The authors provide a high-performance GPU-accelerated implementation of GBRL that is specifically optimized for RL tasks. This library integrates seamlessly with popular RL repositories, such as Stable-baselines3, facilitating ease of adoption and experimentation for RL practitioners.
Experimental Evaluation & Results
Experiments were designed to address key questions around the efficacy of GBRL:
- Can GBT effectively approximate functions in RL, and how does its performance compare with NN-based approaches?
- Does GBRL offer distinct advantages in environments with structured or categorical features?
Classic and High-Dimensional Vectorized Environments
In classic control tasks and some vectorized environments, the results indicate that while both NN and GBRL can achieve competitive performance, the choice of algorithmic backend (PPO, A2C, AWR) significantly influences results. Specifically, PPO emerges as the most effective algorithm when used with GBRL.
Categorical Environments
In environments characterized by structured and categorical data (e.g., MiniGrid), GBRL significantly outperforms traditional NN approaches. This highlights the potential of GBTs in RL tasks that involve complex structured data, aligning with their recognized strengths in supervised learning for similar data types. Particularly notable is PPO GBRL's superior performance in these domains, demonstrating its robustness in handling structured data effectively.
Implications & Future Directions
Practical Applications
The integration of GBTs into RL, as demonstrated by GBRL, opens up several practical applications. Given the inherent interpretability and efficiency of GBTs, GBRL is well-suited for domains where these characteristics are crucial, such as finance, healthcare, and edge computing. Furthermore, the reduction in memory and computational overhead due to the shared architecture makes it viable for deployment on low-compute devices.
Theoretical Implications
The results from GBRL suggest a re-evaluation of function approximators in RL tasks. While NNs have been the de facto standard due to their flexibility and power in high-dimensional spaces, GBTs offer a compelling alternative in structured environments. This embodies a shift towards more diversified and task-specific tools in the RL practitioner's toolkit.
Future Developments
The work identifies several avenues for further research to overcome current limitations:
- Managing Ensemble Growth: As the ensemble of trees grows unbounded with continuous learning steps, exploring strategies for ensemble compression and pruning could lead to significant gains in efficiency and scalability.
- Integration with Modern RL Algorithms: Extending GBRL to support differentiable Q-function-based algorithms, such as DDPG and SAC, will require innovative solutions to integrate non-differentiable GBTs.
- Probabilistic Trees and Ensemble Optimization: Developing probabilistic tree methods and optimizing ensemble size dynamically will further enhance the applicability of GBRL to a broader range of RL tasks.
By addressing these challenges, GBRL has the potential to greatly expand the applicability of RL methods, particularly in real-world scenarios where structured and categorical data are prevalent.
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