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Regularized Hierarchical Policies for Compositional Transfer in Robotics

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Jun 27 2019 DeepMind

Data-efficiency is one of the principal challenges for applying reinforcement learning on physical systems. We use hierarchical models to strengthen transfer while mitigating negative interference - saving weeks of training time for physical robots. https://arxiv.org/abs/1906.11228 https://t.co/6ksuaagS7j
1 replies, 390 likes


Jun 28 2019 Markus Wulfmeier

Proud to announce our recent work on compositional, hierarchical models to strengthen #transfer between related tasks while mitigating negative interference. We considerably improve #dataefficiency for reinforcement learning on physical #robots (reducing training time by weeks)
3 replies, 34 likes


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