Search Results for author: Ronald S. Fearing

Found 3 papers, 2 papers with code

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

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.

Continuous Control Meta-Learning +5

Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

no code implementations14 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.

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

8 code implementations8 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

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