arXiv:2606.27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs. The focus is restricted to a ``pure'' regret benchmark, that compares the performance of the learning algorithm to the best \emph{pure policy} which -- akin to optimal policies of stochastic bandits -- picks the optimal arm from start to finish without ever switching. We introduce a generalization of rested Markovian bandits, \emph{self-degrading Markovian bandits}, for which pure policies a
This paper represents foundational research in reinforcement learning, pushing the boundaries of algorithmic efficiency and performance in complex, partially observable environments relevant to AI agent development.
Improved algorithms for learning in non-observable states and constrained environments directly contribute to more capable and autonomous AI systems, impacting fields from robotics to financial trading.
This research provides a new theoretical framework ('self-degrading Markovian bandits') that could lead to more robust and efficient AI agents operating with incomplete information and under specific operational restrictions.
- · AI researchers
- · Robotics developers
- · Autonomous systems sector
- · Reinforcement learning platforms
- · Legacy decision-making systems
- · Human-in-the-loop oversight in increasingly automated tasks
More efficient and reliable autonomous agents capable of operating in complex, real-world conditions with partial information.
Acceleration of AI adoption in industries requiring adaptive decision-making, such as logistics, healthcare diagnostics, and complex resource management.
Enhanced AI capabilities contributing to broader applications of autonomous systems that can manage uncertainty, reducing traditional human oversight in specialized roles.
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