Prudent-Banker: No Extra Fees for Baseline Safety in Adversarial Bandits With and Without Delays
arXiv:2605.23351v1 Announce Type: new Abstract: We study adversarial multi-armed bandits with and without delayed feedback under a safety-aware goal: achieving minimax-optimal worst-case regret while keeping nearly constant regret relative to a designated "safe" baseline policy. Existing approaches can balance this trade-off with immediate feedback for smooth comparators, but arbitrary delays can mistime transitions between conservatism and exploration, endangering the safety guarantee. To bridge this gap, we propose Prudent-Banker, a novel algorithm that combines a delay-adapted variant of On
The continuous advancements in AI research, particularly in addressing robust decision-making under uncertainty, drive the development of algorithms like Prudent-Banker.
This research is crucial for deploying AI agents in real-world scenarios where safety guarantees and optimal performance under delayed or adversarial conditions are paramount.
The Prudent-Banker algorithm specifically addresses safety in adversarial multi-armed bandits with delayed feedback, previously a significant challenge for AI robustness.
- · AI algorithm developers
- · Robotics and autonomous systems
- · Financial trading platforms
- · Online advertising platforms
- · Systems lacking robust safety-aware AI
- · Traditional reinforcement learning algorithms
Improved safety and reliability of AI agents operating in dynamic and uncertain environments.
Accelerated adoption of AI in critical applications where safety is non-negotiable, such as autonomous vehicles or medical systems.
Enhanced trust in AI systems leading to broader integration across various industries, potentially impacting workforce automation and societal structures.
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Read at arXiv cs.LG