arXiv:2501.19401v5 Announce Type: replace Abstract: We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of piecewise stationary bandits without knowledge of the underlying non-stationarity. DAL accepts any stationary bandit algorithm with order-optimal regret as input and augments it with a change detector, enabling applicability to all common bandit variants. Extensive experimentation demonstrates that DAL consistently surpasses all state-of-the-art methods across diverse non-stationary scenarios, including synthetic benchmarks and real-worl
The paper addresses a critical challenge in real-world AI applications by enabling bandit algorithms to adapt to non-stationary environments more effectively and practically.
This framework offers a significant advancement in the robustness and applicability of AI systems, particularly in areas requiring continuous learning and adaptation to changing conditions.
AI systems can now be deployed in dynamic environments with greater confidence, as their learning algorithms can intrinsically detect and adapt to shifts without prior knowledge of non-stationarity.
- · AI/ML researchers
- · Developers of adaptive AI systems
- · Industries utilizing reinforcement learning
- · Systems reliant on static models
- · Algorithms that struggle with non-stationarity
Improved performance and broader application of bandit algorithms in real-time decision-making systems.
Accelerates the development of more resilient and autonomous AI agents capable of operating in complex, unpredictable environments.
Potentially reduces the human oversight required for maintaining AI system performance in dynamic operational settings.
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Read at arXiv cs.LG