Summary of Sumo: Search-based Uncertainty Estimation For Model-based Offline Reinforcement Learning, by Zhongjian Qiao et al.
SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning
by Zhongjian Qiao, Jiafei Lyu, Kechen Jiao, Qi Liu, Xiu Li
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Offline reinforcement learning (RL) faces challenges due to limited static datasets. Model-based offline RL generates synthetic samples using dynamics models, enhancing overall performance. To evaluate reliability, uncertainty estimation methods are employed. However, model ensemble, commonly used, may not be the best choice. This paper proposes Search-based Uncertainty estimation method for Model-based Offline RL (SUMO) as an alternative. SUMO characterizes uncertainty by measuring cross-entropy against in-distribution dataset samples and uses an efficient search-based method. SUMO provides trustworthy uncertainty estimation. The authors integrate SUMO into model-based offline RL algorithms, including MOPO and AMOReL, and provide theoretical analysis. Experimental results on D4RL datasets demonstrate that SUMO achieves more accurate uncertainty estimation and boosts algorithm performance. These findings indicate that SUMO can be a better uncertainty estimator for model-based offline RL in reward penalty or trajectory truncation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make machines learn from data without actually using the data itself. They want to use fake data instead, which is generated by a model of the real world. But they need to be sure that this fake data is good and not too different from the real thing. The new method they propose is called SUMO, which helps them figure out how likely it is that something will happen based on the fake data. They tested this method with some algorithms and found that it works better than other methods. This could help machines learn more efficiently and accurately. |
Keywords
» Artificial intelligence » Cross entropy » Reinforcement learning
