Locality sets the fundamental round lower bound L_ε = floor(log(1/2ε)/log(1/γ)) for ε-accuracy on large-diameter graphs; direct propagation achieves it while gossip averaging pays extra 1/gap(W) factors.
Offline reinforcement learning with implicit Q -learning
2 Pith papers cite this work. Polarity classification is still indexing.
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A new RL framework for chronic disease management compresses time-to-control using clinician capability weighting and action intensity constraints, yielding 15 percentage point gains on synthetic type 2 diabetes simulations over standard offline RL.
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Locality, Not Spectral Mixing, Governs Direct Propagation in Distributed Offline Dynamic Programming
Locality sets the fundamental round lower bound L_ε = floor(log(1/2ε)/log(1/γ)) for ε-accuracy on large-diameter graphs; direct propagation achieves it while gossip averaging pays extra 1/gap(W) factors.
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Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management
A new RL framework for chronic disease management compresses time-to-control using clinician capability weighting and action intensity constraints, yielding 15 percentage point gains on synthetic type 2 diabetes simulations over standard offline RL.