Scalable reward learning from demonstration
Name
How_Scalable reward.pdf
Size
1.5 MB
Format
Adobe PDF
Checksum (MD5)
0809e5bcdf94f121f991c8b1acc357c1
Author(s)
Michini, Bernard J.
How, Jonathan P.
Cutler, Mark Johnson
Date Issued
May 2013
Journal
Proceedings of the 2013 IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Michini, Bernard, Mark Cutler, and Jonathan P. How. “Scalable Reward Learning from Demonstration.” 2013 IEEE International Conference on Robotics and Automation (May 2013).
Version
Author's final manuscript
Abstract
Reward learning from demonstration is the task of inferring the intents or goals of an agent demonstrating a task. Inverse reinforcement learning methods utilize the Markov decision process (MDP) framework to learn rewards, but typically scale poorly since they rely on the calculation of optimal value functions. Several key modifications are made to a previously developed Bayesian nonparametric inverse reinforcement learning algorithm that avoid calculation of an optimal value function and no longer require discretization of the state or action spaces. Experimental results given demonstrate the ability of the resulting algorithm to scale to larger problems and learn in domains with continuous demonstrations.
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
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.2013.6630592
