Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation

Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation

Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success in many complex tasks, these algorithms need a large number of samples to learn meaningful policies. In this paper...

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Bibliographic Details
Main Authors: Ota, Kei, Jha, Devesh K, Romeres, Diego, van Baar, Jeroen, Smith, Kevin A, Semitsu, Takayuki, Oiki, Tomoaki, Sullivan, Alan, Nikovski, Daniel, Tenenbaum, Joshua B
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access:https://hdl.handle.net/1721.1/138371

Internet

https://hdl.handle.net/1721.1/138371

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