101 citations · 237 across the 8 of their papers we have counts for
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cs.LG2018★ 101 cited
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu +4
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and…
cs.LG2017★ 5 cited
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing +1
Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to…