Summary of Uncertainty-based Offline Variational Bayesian Reinforcement Learning For Robustness Under Diverse Data Corruptions, by Rui Yang et al.
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions
by Rui Yang, Jie Wang, Guoping Wu, Bin Li
First submitted to arxiv on: 1 Nov 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 TRACER method addresses a significant challenge in offline reinforcement learning by introducing Bayesian inference to capture uncertainty caused by diverse data corruptions. The approach models all corruptions as uncertainty in the action-value function, using offline data to approximate the posterior distribution under a Bayesian framework. This allows TRACER to distinguish corrupted data from clean data and regulate the loss associated with corrupted data to enhance robustness and performance in clean environments. Experiments demonstrate that TRACER outperforms state-of-the-art approaches across both individual and simultaneous data corruptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRACER is a new way to make robots smarter by fixing a big problem they have. When robots learn from data, it’s often not perfect because of things like noise or attacks. This makes them not work well in real-life situations. The TRACER method helps solve this by using math to figure out what’s wrong with the data and ignore the bad parts. It works really well and can even tell when some of the data is fake. This makes it a big improvement over old ways of doing things. |
Keywords
» Artificial intelligence » Bayesian inference » Reinforcement learning
