Summary of Achieving Constant Regret in Linear Markov Decision Processes, by Weitong Zhang and Zhiyuan Fan and Jiafan He and Quanquan Gu
Achieving Constant Regret in Linear Markov Decision Processes
by Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu
First submitted to arxiv on: 16 Apr 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 A new reinforcement learning (RL) algorithm called Cert-LSVI-UCB is introduced for misspecified linear Markov decision processes (MDPs). The goal is to design an algorithm that incurs finite regret over infinite episodes with high probability. Cert-LSVI-UCB uses a method for multi-phase value-targeted regression, enabling a fine-grained concentration analysis and establishing instance-dependent regret bounds. Specifically, the algorithm has a cumulative regret of O(d3H5/Δ) with high probability, where d is the feature space dimension, H is the horizon, Δ is the suboptimality gap, and ζ is the misspecification level. This regret bound is independent of the number of episodes K. Cert-LSVI-UCB is the first algorithm to achieve a constant, instance-dependent, high-probability regret bound in RL with linear function approximation without relying on prior distribution assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI algorithm called Cert-LSVI-UCB helps machines learn from experience. It’s designed for certain types of decision-making problems where there are some unknowns. The goal is to make good decisions while learning, even when the rules change. This algorithm is special because it can predict how well it will do in advance, and it doesn’t need to know what might happen next. This is useful for making smart choices in situations where things aren’t always certain. |
Keywords
» Artificial intelligence » Probability » Regression » Reinforcement learning
