arXiv:2606.31769v1 Announce Type: new Abstract: We study policy optimization for online episodic tabular Markov decision processes with unknown transition kernels, aiming for best-of-both-worlds guarantees together with data-dependent regret bounds. Recent work (Dann et al., 2023; Li et al., 2026) has shown that policy optimization can adapt to both adversarial and stochastic losses with first-order, second-order, and path-length bounds, but only under known transitions, leaving open whether such data-dependent guarantees are achievable by policy optimization when the transition kernel is unkn
This research addresses a fundamental limitation in reinforcement learning, pushing the boundaries of policy optimization in complex, real-world scenarios where transition dynamics are unknown. The work builds on recent advancements in data-dependent regret bounds.
Improved policy optimization in unknown environments is critical for developing more robust and autonomous AI systems, which can adapt effectively without prior complete knowledge of their surroundings or system function. This will accelerate the deployment of AI in dynamic and unstructured settings.
AI systems will be able to learn and optimize policies more efficiently in environments where transition models are not perfectly understood or are constantly changing, reducing the need for extensive pre-training or manual environment modeling. This removes a significant friction point for real world deployment.
- · AI/ML researchers
- · Robotics companies
- · Autonomous systems developers
- · Logistics and supply chain optimization
- · Companies relying on static AI models
- · Manual policy optimization methods
More adaptive and robust AI agents become feasible across various applications.
Accelerated development and adoption of AI in industries requiring real-time decision-making in uncertain environments.
New economic efficiencies due to AI systems capable of autonomous self-improvement and adaptation in operational contexts.
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Read at arXiv cs.LG