arXiv:2409.05980v2 Announce Type: replace-cross Abstract: Rested and Restless Bandits are two well-known bandit settings that are useful to model real-world sequential decision-making problems in which the expected reward of an arm evolves over time due to the actions we perform or due to the nature. In this work, we propose Graph-Triggered Bandits (GTBs), a unifying framework to generalize and extend rested and restless bandits. In this setting, the evolution of the arms' expected rewards is governed by a graph defined over the arms. An edge connecting a pair of arms $(i,j)$ represents the fa
This paper introduces a novel theoretical framework to generalize existing multi-armed bandit problems, driven by ongoing research to improve decision-making algorithms in dynamic environments.
Advanced bandit algorithms and their generalizations are crucial for optimizing sequential decision-making in various applications, improving efficiency and effectiveness in complex systems.
The proposed Graph-Triggered Bandits (GTBs) offer a more adaptable and comprehensive model for scenarios where rewards evolve based on interdependencies between choices, potentially leading to more sophisticated AI agents.
- · AI researchers
- · Developers of sequential decision-making systems
- · Industries relying on adaptive optimization
- · Systems using less sophisticated bandit algorithms
The new GTB framework provides a more robust mathematical foundation for modeling interactive decision environments.
This improved theoretical understanding could lead to the development of more versatile and intelligent AI agents capable of handling complex, interconnected decision spaces.
Broader adoption of GTB-like algorithms could enhance the autonomy and efficiency of AI systems across domains such as resource allocation, recommendation engines, and dynamic pricing.
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