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GBRL is a Python-based Gradient Boosting Trees (GBT) library, similar to popular packages such as XGBoost, CatBoost, but specifically designed and optimized for reinforcement learning (RL). GBRL is implemented in C++/CUDA aimed to seamlessly integrate within popular RL libraries.
GBRL adapts the power of Gradient Boosting Trees to the unique challenges of RL environments, including non-stationarity and the absence of predefined targets. The following diagram illustrates how GBRL uses gradient boosting trees in RL:
GBRL features a shared tree-based structure for policy and value functions, significantly reducing memory and computational overhead, enabling it to tackle complex, high-dimensional RL problems.
The following results, obtained using the GBRL_SB3
repository, demonstrate the performance of PPO with GBRL compared to neural-networks across various scenarios and environments:
GBRL provides pre-compiled binaries for easy installation. Choose one of the following options:
CPU-only installation (default):
pip install gbrl
GPU-enabled installation (requires CUDA 12 runtime libraries):
pip install gbrl-gpu
For further installation details and dependencies see the documentation.
For a detailed usage example, see tutorial.ipynb
For comprehensive documentation, visit the GBRL documentation.
@article{gbrl,
title={Gradient Boosting Reinforcement Learning},
author={Benjamin Fuhrer, Chen Tessler, Gal Dalal},
year={2024},
eprint={2407.08250},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.08250},
}
Copyright © 2024, NVIDIA Corporation. All rights reserved.
This work is made available under the NVIDIA Source Code License-NC. Click here. to view a copy of this license.
FAQs
Gradient Boosted Trees for RL
We found that gbrl demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Socket now supports Scala and Kotlin, bringing AI-powered threat detection to JVM projects with easy manifest generation and fast, accurate scans.
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