Summary of Sambo-rl: Shifts-aware Model-based Offline Reinforcement Learning, by Wang Luo et al.
SAMBO-RL: Shifts-aware Model-based Offline Reinforcement Learning
by Wang Luo, Haoran Li, Zicheng Zhang, Congying Han, Jiayu Lv, Tiande Guo
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper tackles the challenges of distribution shift in model-based offline reinforcement learning by analyzing the problem into two fundamental components: model bias and policy shift. The authors demonstrate how these factors distort value estimation and restrict policy optimization. To address this, they propose a novel Shifts-aware Reward (SAR) through a unified probabilistic inference framework that refines value learning and facilitates policy training. Building on SAR, they introduce SAMBO-RL, a practical framework for efficiently training classifiers to approximate SAR for policy optimization. Empirical experiments show that SAR effectively mitigates distribution shift, and SAMBO-RL achieves superior or comparable performance across various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model-based offline reinforcement learning trains policies using pre-collected datasets and learned environment models without requiring direct real-world interaction. The paper investigates how existing methods address the problem of distribution shift (DS) but often result in inconsistent objectives and lack a unified theoretical foundation. The authors reveal that DS distorts value estimation and restricts policy optimization, proposing a novel Shifts-aware Reward (SAR) to refine value learning and facilitate policy training. They also introduce SAMBO-RL, a practical framework for efficiently training classifiers to approximate SAR for policy optimization. |
Keywords
» Artificial intelligence » Inference » Optimization » Reinforcement learning
