Summary of Solving Offline Reinforcement Learning with Decision Tree Regression, by Prajwal Koirala and Cody Fleming
Solving Offline Reinforcement Learning with Decision Tree Regression
by Prajwal Koirala, Cody Fleming
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 study introduces a novel approach to addressing offline reinforcement learning (RL) problems by reframing them as regression tasks that can be effectively solved using Decision Trees. The authors present two distinct frameworks: return-conditioned and return-weighted decision tree policies (RCDTP and RWDTP), which achieve notable speed in agent training and inference. These frameworks demonstrate performance at least on par with established methods, evaluated on D4RL datasets for locomotion, manipulation, wheeled robots, flying robots, and delayed/sparse reward scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is a challenging problem that requires agents to learn from experience without receiving immediate rewards. This study proposes a simple yet effective solution by redefining offline RL as a regression task using Decision Trees. The authors develop two new frameworks that can quickly train and deploy agents, achieving results comparable to established methods. This breakthrough has implications for robotics and AI applications where timely decision-making is crucial. |
Keywords
* Artificial intelligence * Decision tree * Inference * Regression * Reinforcement learning
