Olaf's opportunistic in-network aggregation and replacement of asynchronous DRL updates reduces model staleness and speeds up convergence under congestion.
Trading latency for compute in the net- work
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Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning
Olaf's opportunistic in-network aggregation and replacement of asynchronous DRL updates reduces model staleness and speeds up convergence under congestion.