Summary of Sample Complexity Of Offline Distributionally Robust Linear Markov Decision Processes, by He Wang et al.
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
by He Wang, Laixi Shi, Yuejie Chi
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST)
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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 new offline reinforcement learning (RL) approach tackles the sim-to-real gap by developing a sample-efficient method for endowing learned policies with robustness in high-dimensional state-action spaces. The study focuses on distributionally robust linear Markov decision processes (MDPs) with uncertainty sets characterized by total variation distance using offline data. A pessimistic model-based algorithm is introduced, and its sample complexity bound is established under minimal data coverage assumptions, outperforming prior art by at least O(d), where d is the feature dimension. The proposed algorithm’s performance guarantee is further improved through a carefully designed variance estimator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline RL learns to make decisions without trying new things. This can be a problem because learned policies might not work well in real-life situations that are different from the training data. To fix this, researchers developed a new way to train models that performs well even when environments change. The approach uses linear Markov decision processes and characterizes uncertainty sets using total variation distance. A new algorithm is introduced that has a sample complexity bound, meaning it can learn quickly without needing too much data. This outperforms previous methods by at least O(d), where d is the number of features. |
Keywords
* Artificial intelligence * Reinforcement learning
