Teaching agents with human feedback: a demonstration of the TAMER framework

Authors:
W. Bradley Knox;Peter Stone;Cynthia Breazeal
Affiliations:
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;The University of Texas at Austin, Austin, Texas, USA;Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Venue:
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Year:
2013

Citing 7
Cited 0

Teachable robots: Understanding human teaching behavior to build more effective robot learners

Artificial Intelligence
Interactively shaping agents via human reinforcement: the TAMER framework

Proceedings of the fifth international conference on Knowledge capture
Combining manual feedback with subsequent MDP reward signals for reinforcement learning

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Dynamic reward shaping: training a robot by voice

IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Teaching a robot to perform task through imitation and on-line feedback

CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Reinforcement learning from simultaneous human and MDP reward

Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Learning non-myopically from human-generated reward

Proceedings of the 2013 international conference on Intelligent user interfaces

Quantified Score

Hi-index	0.00

Visualization

Abstract

Incorporating human interaction into agent learning yields two crucial benefits. First, human knowledge can greatly improve the speed and final result of learning compared to pure trial-and-error approaches like reinforcement learning. And second, human users are empowered to designate "correct" behavior. In this abstract, we present research on a system for learning from human interaction - the TAMER framework - then point to extensions to TAMER, and finally describe a demonstration of these systems.