Gradient Boosting Reinforcement Learning (2407.08250v2)

Abstract: We present Gradient Boosting Reinforcement Learning (GBRL), a framework that adapts the strengths of gradient boosting trees (GBT) to reinforcement learning (RL) tasks. While neural networks (NNs) have become the de facto choice for RL, they face significant challenges with structured and categorical features and tend to generalize poorly to out-of-distribution samples. These are challenges for which GBTs have traditionally excelled in supervised learning. However, GBT's application in RL has been limited. The design of traditional GBT libraries is optimized for static datasets with fixed labels, making them incompatible with RL's dynamic nature, where both state distributions and reward signals evolve during training. GBRL overcomes this limitation by continuously interleaving tree construction with environment interaction. Through extensive experiments, we demonstrate that GBRL outperforms NNs in domains with structured observations and categorical features while maintaining competitive performance on standard continuous control benchmarks. Like its supervised learning counterpart, GBRL demonstrates superior robustness to out-of-distribution samples and better handles irregular state-action relationships.

• The paper proposes GBRL, a novel framework that integrates Gradient Boosting Trees into reinforcement learning, achieving competitive performance with actor-critic methods.
• The authors introduce a shared tree-based actor-critic architecture that reduces memory and computational demands while approximating policy and value functions effectively.
• Experiments demonstrate that GBRL excels in structured and categorical environments, making it well-suited for deployment on edge devices and real-world applications.

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 field 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

1. 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.
2. 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.
3. 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:

1. 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.
2. 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.
3. 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.