Learning to Walk via Deep Reinforcement Learning

Robotics - Science and Systems, 2019.

Cited by: 52|Bibtex|Views41|DOI:https://doi.org/10.15607/RSS.2019.XV.011
EI
Other Links: arxiv.org|dblp.uni-trier.de

Abstract:

Deep reinforcement learning offers the promise of automatic acquisition of robotic control policies that directly map sensory inputs to low-level actions. In the domain of robotic locomotion, it could make it possible for locomotion skills to be learned with minimal engineering and without even needing to construct a model of the robot....More

Code:

Data:

Full Text
Your rating :
0

 

Tags
Comments