Summary of Uncertainty-aware Distributional Offline Reinforcement Learning, by Xiaocong Chen and Siyu Wang and Tong Yu and Lina Yao
Uncertainty-aware Distributional Offline Reinforcement Learning
by Xiaocong Chen, Siyu Wang, Tong Yu, Lina Yao
First submitted to arxiv on: 26 Mar 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 The proposed uncertainty-aware distributional offline RL method simultaneously addresses epistemic uncertainty and environmental stochasticity by learning risk-averse policies and characterizing the entire distribution of discounted cumulative rewards. This approach is evaluated through comprehensive experiments in both risk-sensitive and risk-neutral benchmarks, demonstrating its superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning uses observational data to learn a policy without trying actions. It’s important to ensure the learned policy is safe by understanding uncertainties around different actions and environmental changes. Traditional methods focus on reducing uncertainty by being cautious, but they often ignore environmental changes. This study proposes a new method that can learn cautious policies and understand the full range of possible outcomes. The method is tested on various benchmarks and shows better results. |
Keywords
* Artificial intelligence * Reinforcement learning
