Summary of Semopo: Learning High-quality Model and Policy From Low-quality Offline Visual Datasets, by Shenghua Wan et al.
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
by Shenghua Wan, Ziyuan Chen, Le Gan, Shuai Feng, De-Chuan Zhan
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper proposes a novel approach to offline reinforcement learning (RL), called Separated Model-based Offline Policy Optimization (SeMOPO). SeMOPO addresses the distribution shift issue in offline RL by decomposing latent states into endogenous and exogenous parts. The model uncertainty is estimated on the endogenous states only, which helps alleviate bias caused by complex distractors with non-trivial dynamics. Theoretical guarantees are provided for model uncertainty and performance bound of SeMOPO. To assess efficacy, the authors construct the Low-Quality Vision Deep Data-Driven Datasets for RL (LQV-D4RL) and experimentally demonstrate that SeMOPO outperforms baseline methods. The project website is available at https://sites.google.com/view/semopo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in artificial intelligence called offline reinforcement learning. It helps machines learn from data without actually doing the tasks themselves. The authors created a new method to make sure the machine learns correctly even when there are distracting things around. They tested this method with images and videos as inputs and showed it works much better than other methods. This breakthrough has many potential applications in real-world scenarios. Check out the project website at https://sites.google.com/view/semopo for more information. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
