Near-optimal Per-Action Regret Bounds for Sleeping Bandits: Algorithmic Analysis and Insights

Alapfogalmak

The authors derive near-optimal per-action regret bounds for sleeping bandits by minimizing the per-action regret directly using specialized algorithms, achieving significant improvements over existing bounds.

Kivonat

The content discusses the derivation of near-optimal per-action regret bounds for sleeping bandits. It introduces new algorithms like SB-EXP3 and FTARL, extending results to bandits with advice from sleeping experts. The analysis provides insights into adaptive and tracking regret bounds in standard non-sleeping bandits.
Key points include:

Introduction to the concept of sleeping bandits in contrast to standard multi-armed bandit frameworks.
Derivation of new algorithms like SB-EXP3 and FTARL for optimizing per-action regret in sleeping bandits.
Extension of results to bandits with advice from sleeping experts, leading to new proofs for adaptive and tracking regret bounds.
Discussion on the implications of these findings on existing minimax optimal algorithms and their limitations.
Exploration of strongly adaptive lower bounds for per-action regret in sleeping bandits, highlighting challenges and theoretical constraints.
Overall, the content provides a comprehensive analysis of algorithmic approaches and their implications on regret bounds in various bandit settings.

Statisztikák

The best known upper bound is O(K √ T A ln K).
New near-optimal bounds are obtained: O(√ T A ln K) and O(p T √ AK).

Idézetek

"We address this gap by directly minimizing the per-action regret using generalized versions of EXP3, EXP3-IX, and FTRL with Tsallis entropy."
"Our work focuses on minimizing the per-action regret in the fully adversarial setting."
"SE-EXP4 obtains near-optimal pseudo-regret and high probability regret bounds as well in the bandits with advice from sleeping experts setting."

Főbb Kivonatok

Near-optimal Per-Action Regret Bounds for Sleeping Bandits

by Quan Nguyen,... : arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01315.pdf

Mélyebb kérdések

How do these new algorithms impact real-world applications that involve dynamic sets of available options

The development of new algorithms for sleeping bandits with near-optimal per-action regret bounds has significant implications for real-world applications that involve dynamic sets of available options. In scenarios where the availability or relevance of choices changes over time, such as drug testing, recommender systems, or financial trading, these algorithms can adapt to varying conditions and make more informed decisions. For example, in drug testing where different drugs may only be available at certain times or when new drugs are introduced into the market, these algorithms can efficiently learn and optimize outcomes based on the changing set of options.

What are the practical implications of achieving near-optimal per-action regret bounds in sleeping bandit scenarios

Achieving near-optimal per-action regret bounds in sleeping bandit scenarios has several practical implications. Firstly, it allows decision-makers to minimize their regrets by selecting actions that lead to better outcomes even when faced with uncertainty about which arms will be active in each round. This is crucial in situations where resources are limited or costly mistakes need to be avoided.
Furthermore, these findings enable algorithm designers and practitioners to develop more effective strategies for learning from data streams with evolving sets of options. By optimizing regret bounds in sleeping bandits settings, organizations can improve their decision-making processes and achieve better results over time.

How can these findings be applied to enhance decision-making processes beyond traditional bandit frameworks

The findings regarding near-optimal per-action regret bounds in sleeping bandit scenarios have broader applications beyond traditional bandit frameworks. These insights can be applied to enhance decision-making processes across various domains such as online advertising optimization, personalized recommendation systems, clinical trials design, autonomous vehicle navigation systems, and cybersecurity threat detection.
By incorporating these advanced algorithms into existing models and frameworks used for decision-making under uncertainty or dynamic environments, organizations can improve efficiency and effectiveness in resource allocation strategies while minimizing potential losses due to suboptimal decisions. The ability to adapt quickly to changing circumstances while maintaining performance levels is a valuable asset in today's fast-paced and unpredictable business landscape.