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.
Learning near-optimal policies with B ellman-residual minimization based fitted policy iteration and a single sample path
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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.