<ul><li>Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference</li><li>Realtime environments change as agents perform action inference and learning, requiring high interaction frequencies to minimize regret.</li><li>Recent advances in machine learning involve larger neural networks with longer inference times, raising concerns about their applicability in realtime systems.</li><li>Proposed algorithms for staggering asynchronous inference processes ensure consistent time intervals for actions, enabling use of models with high inference times.</li></ul>

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

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