Summary of Minimax Optimal and Computationally Efficient Algorithms For Distributionally Robust Offline Reinforcement Learning, by Zhishuai Liu et al.
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
by Zhishuai Liu, Pan Xu
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel framework for distributionally robust offline reinforcement learning (RL) is proposed to model dynamics uncertainty in large state-action spaces. This approach requires function approximations, but the consideration of dynamics uncertainty introduces nonlinearity and computational burden, posing challenges for analysis and practical implementation. In a basic setting with linearly parameterized nominal and perturbed models, minimax optimal and computationally efficient algorithms are developed for function approximation and instance-dependent suboptimality analysis. The results show that function approximation in robust offline RL is distinct from and potentially harder than in standard offline RL. Key contributions include a novel variance-aware function approximation mechanism, uncertainty decomposition procedure, and quantification of robust value function shrinkage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Robust offline reinforcement learning wants to make sure policies work well even if the environment changes. To do this, we need to model how these changes can happen. When there are many things that can change (like in a big video game), it gets harder to design an algorithm. We focus on a simple case where everything is linear and show that robust offline RL is more complicated than regular offline RL. Our approach has new ideas like using variance information and breaking down uncertainty, which could be useful elsewhere. |
Keywords
* Artificial intelligence * Reinforcement learning
