AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work extends the concept of gadget game, tabular technique for test-time search, to the reinforcement learning setting and formally proves that, unlike prior tabular algorithms, regularized policy-gradient algorithms limit possible strategy degradation caused by test-time reasoning, even without the gadget games.
NashDreamer is proposed, a principled MBRL framework for two-player zero-sum IIGs that introduces a centralized Multi-Agent Recurrent State-Space Model (MARSSM) that decouples environment dynamics from the effect of players's strategies on their individual observations.
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