Summary of Sample Complexity Characterization For Linear Contextual Mdps, by Junze Deng et al.
Sample Complexity Characterization for Linear Contextual MDPs
by Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper explores contextual Markov decision processes (CMDPs), a framework for modeling reinforcement learning problems with time-varying environments. Specifically, the authors investigate two linear function approximation models: Model I, which uses context-varying representations and common linear weights; and Model II, which employs common representations and context-varying linear weights. The researchers propose novel model-based algorithms for both models, demonstrating guaranteed ε-suboptimality gaps with polynomial sample complexity. Notably, the first model improves upon existing results by removing the reachability assumption in tabular CMDPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, experts study a type of decision-making problem that changes over time. They look at two ways to simplify complex problems: using similar features for all situations (Model I) or different weights for each situation (Model II). The researchers create new algorithms for both models and show they can find good solutions with fewer data than before. |
Keywords
* Artificial intelligence * Reinforcement learning
