arXiv:2606.02232v1 Announce Type: new Abstract: Learning a Markov transition model is not merely conditional density estimation: the learned object must be a valid transition kernel before it is iterated in downstream dynamics. This paper introduces a Doeblin-anchored contrastive chart, a statistical-to-dynamical coordinate framework for learning transition kernels from contrastive objectives. Given a restart law and an anchor strength, the chart mixes the target transition with the restart law. The resulting anchored kernel is simultaneously a Doeblin-minorized Markov kernel, the positive con
This research introduces a novel theoretical framework for learning Markov transition kernels, a foundational component for advanced AI systems, particularly in reinforcement learning and agentic behavior.
Improving the robustness and validity of learned Markov transition models is crucial for developing more reliable and predictable AI agents, impacting autonomous decision-making and control systems.
The proposed 'Doeblin-anchored contrastive chart' offers a new, more principled approach to ensuring learned transition models are mathematically sound for iteration in dynamic systems.
- · AI researchers
- · Reinforcement learning engineers
- · Autonomous systems developers
- · Developers using less robust, ad-hoc transition model learning methods
More mathematically sound and reliable models for training AI agents become available.
This could lead to a faster path to deployment for AI systems requiring robust predictive capabilities and sequential decision-making.
Improved fundamental AI models may indirectly accelerate the development of more complex and generalizable AI agents.
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Read at arXiv cs.LG