AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Reinforcement learning in safety-critical domains requires maximizing task performance while strictly adhering to safety constraints. Existing safe reinforcement learning paradigms typically force a trade-off: they either require a priori knowledge to provide strict safety guarantees (e.g., safety filters), or they ena...
This work formalizes RL safety with a safety-critical Markov decision process (SC-MDP), which requires categorical, rather than high-probability, avoidance of catastrophic failure states and provides a simple, principled recipe for safe RL: train and deploy RL policies with the most permissive safety filter that is ava...
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