5.2k citations · 37.6k across the 56 of their papers we have counts for
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cs.LG2015★ 83 cited
Policy Distillation
Andrei A. Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre +6
Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specif…
cs.LG2015★ 401 cited
Massively Parallel Methods for Deep Reinforcement Learning
Arun Nair, Praveen Srinivasan, Sam Blackwell +11
We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; p…