Summary of Gradient Boosting Reinforcement Learning, by Benjamin Fuhrer et al.
Gradient Boosting Reinforcement Learning
by Benjamin Fuhrer, Chen Tessler, Gal Dalal
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Gradient-Boosting RL (GBRL) framework extends the advantages of Gradient Boosting Trees to reinforcement learning (RL), addressing limitations in applying GBTs to online learning scenarios. By introducing a tree-sharing approach for policy and value functions, GBRL achieves competitive performance across diverse tasks, excelling in domains with structured or categorical features. The framework also includes a high-performance, GPU-accelerated implementation that integrates seamlessly with widely-used RL libraries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GBRL is a new way to do reinforcement learning using Gradient Boosting Trees. It’s like a superpower for robots and computers that need to learn from their mistakes. GBRL makes it easier to solve problems where there are lots of rules or categories, like playing chess or recognizing animals in pictures. It also works fast and efficient on special computers called GPUs. |
Keywords
* Artificial intelligence * Boosting * Online learning * Reinforcement learning
