Efficient reinforcement learning for robots using informative simulated priors
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How_Efficient reinforcement.pdf
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679.44 KB
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Adobe PDF
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Author(s)
Cutler, Mark Johnson
How, Jonathan P
Date Issued
July 2015
Journal
2015 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Cutler, Mark and How, Jonathan P. “Efficient Reinforcement Learning for Robots Using Informative Simulated Priors.” 2015 IEEE International Conference on Robotics and Automation (ICRA), May 26-30 2015, Seattle, Washington, Institute of Electrical and Electronics Engineers (IEEE), July 2015 © 2015 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
Autonomous learning through interaction with the physical world is a promising approach to designing controllers and decision-making policies for robots. Unfortunately, learning on robots is often difficult due to the large number of samples needed for many learning algorithms. Simulators are one way to decrease the samples needed from the robot by incorporating prior knowledge of the dynamics into the learning algorithm. In this paper we present a novel method for transferring data from a simulator to a robot, using simulated data as a prior for real-world learning. A Bayesian nonparametric prior is learned from a potentially black-box simulator. The mean of this function is used as a prior for the Probabilistic Inference for Learning Control (PILCO) algorithm. The simulated prior improves the convergence rate and performance of PILCO by directing the policy search in areas of the state-space that have not yet been observed by the robot. Simulated and hardware results show the benefits of using the prior knowledge in the learning framework.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2015.7139550
