arXiv:2606.02363v1 Announce Type: new Abstract: We study sequential decision-making in partially observable environments against strategic, adaptive opponents, modeled as partially observable Markov games (POMGs). The central challenge is to learn latent dynamics from partial observations while facing an adversary whose behavior depends on the learner's strategy, making standard regret notions inadequate. We prove that an epoch-based optimistic maximum-likelihood algorithm achieves $\tilde{O}(\sqrt{T})$ policy regret for fixed problem parameters, with explicit dependence on the horizon, advers
The continuous academic advancements in AI, particularly in reinforcement learning and multi-agent systems, drive ongoing research into complex decision-making environments.
This work is critical for developing more robust and intelligent AI agents capable of operating effectively in dynamic, adversarial, and partially observable real-world settings, which is essential for numerous strategic applications.
The theoretical foundation for designing AI systems that can learn and adapt optimally against strategic opponents in uncertain environments becomes stronger, potentially leading to more reliable and generalizable AI agent architectures.
- · AI research labs
- · Defense contractors
- · Autonomous systems developers
- · Organizations relying on simple heuristic AI
- · Adversaries unprepared for sophisticated AI
Improved performance of AI agents in complex strategic games and partially observable real-world tasks.
Accelerated development of autonomous systems in domains like cyber security, financial trading, and defense.
Potential for an arms race in AI agent capabilities, particularly in strategic and adversarial applications.
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Read at arXiv cs.LG