arXiv:2602.01903v2 Announce Type: replace Abstract: This work studies online episodic tabular Markov decision processes (MDPs) with known transitions and develops best-of-both-worlds algorithms that achieve refined data-dependent regret bounds in the adversarial regime and variance-dependent regret bounds in the stochastic regime. We quantify MDP complexity using a first-order quantity and several new data-dependent measures for the adversarial regime, including a second-order quantity and a path-length measure, as well as variance-based measures for the stochastic regime. To adapt to these me
This research continues the ongoing effort in machine learning to develop more robust and efficient algorithms for sequential decision-making, driven by the increasing complexity and uncertainty of real-world AI applications.
Improved regret bounds and data-dependent measures for online MDPs can lead to more reliable and adaptive AI systems, especially in applications where decisions are made sequentially under varying conditions.
The development of 'best-of-both-worlds' algorithms that adapt to both adversarial and stochastic regimes offers a more nuanced understanding and control over AI agent performance in unpredictable environments.
- · AI researchers
- · Reinforcement learning practitioners
- · Sectors using AI for sequential decision-making
- · Developers of less adaptive sequential decision-making algorithms
More efficient and resilient AI agents in dynamic environments become possible.
This could accelerate the deployment of AI in mission-critical applications requiring high adaptability and guaranteed performance.
Fundamental advancements in AI's ability to handle uncertainty might lead to novel agent architectures and widespread adoption across diverse industries.
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