most citedRL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads

4 citations · 4 across the 2 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2022

Optimizing Data Collection in Deep Reinforcement Learning

James Gleeson, Daniel Snider, Yvonne Yang +3

Reinforcement learning (RL) workloads take a notoriously long time to train due to the large number of samples collected at run-time from simulators. Unfortunately, cluster scale-u…

cs.LG2021★ 4 cited

RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads

James Gleeson, Srivatsan Krishnan, Moshe Gabel +3

Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications. Unfortunately, system-level bottlenecks in RL work…