105 citations · 115 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 10 cited
Hierarchical Policy Learning is Sensitive to Goal Space Design
Zach Dwiel, Madhavun Candadai, Mariano Phielipp +1
Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding improved sample efficiency, the ability to deal with long time horizons and transfera…
cs.DC2018★ 105 cited
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla +18
The Deep Learning (DL) community sees many novel topologies published each year. Achieving high performance on each new topology remains challenging, as each requires some level of…