2 code implementations • ICLR 2019 • Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.
no code implementations • 14 Nov 2017 • Anusha Nagabandi, Guangzhao Yang, Thomas Asmar, Ravi Pandya, Gregory Kahn, Sergey Levine, Ronald S. Fearing
We present an approach for controlling a real-world legged millirobot that is based on learned neural network models.
8 code implementations • 8 Aug 2017 • Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine
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 achieve good performance.
Model-based Reinforcement Learning reinforcement-learning +1