arXiv:2409.18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret. This variant of the multi-armed bandit problem elegantly amalgamates two of its most ubiquitous objectives: regret minimization and BAI. More precisely, the agent's goal is to identify the best arm with a prescribed confidence level $\delta$, while minimizing the cumulative regret up to the stopping time. Focusing on single-parameter exponential families of distributions, we leverage
The paper addresses a critical theoretical challenge in AI, motivated by the increasing need for responsible and efficient experimentation in real-world AI applications.
Improving Best Arm Identification with minimal regret fundamentally enhances the efficiency and safety of AI systems that learn through sequential decision-making, impacting fields from medical trials to reinforcement learning.
This research provides a more robust theoretical framework for AI systems to optimize exploration-exploitation trade-offs, leading to faster learning and reduced costs in practical deployments.
- · AI researchers
- · Reinforcement learning applications
- · Drug discovery
- · Clinical trials
- · Inefficient experimental designs
- · Trial-and-error based systems
More efficient and reliable AI decision-making systems will emerge across various industries.
This efficiency gain could accelerate the development and deployment of AI agents in complex environments.
The reduced cost of experimentation might lower barriers to entry for AI innovation in areas requiring extensive real-world testing.
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