Object-Focused Advice in Reinforcement Learning
Authors: 
Samantha Krening, Brent Harrison, Karen M. Feigh, Charles Isbell, Andrea ThomazAuthors Info & Claims
Samantha Krening, Brent Harrison, Karen M. Feigh, Charles Isbell, Andrea ThomazAuthors Info & ClaimsPages 1447 - 1448
Abstract
In order for robots and intelligent agents to interact with and learn from people with no machine-learning expertise, robots should be able to learn from natural human instruction. Many human explanations consist of simple sentences without state information, yet most machine learning techniques that incorporate human guidance cannot use non-specific explanations. This work aims to learn policies from a few sentences that aren't state specific. The proposed Object-focused advice links an object to an action, and allows a person to generalize over an object's state space. To evaluate this technique, agents were trained using Object-focused advice collected from participants in an experiment in the Mario Bros. domain. The results show that Object-focused advice performs better than when no advice is given, the agent can learn where to apply the advice in the state space, and the agent can recover from adversarial advice. Also, including warnings of what not do to in addition to advice of what actions to take improves performance.
References
[1]
B. D. Argall, S. Chernova, M. Veloso, and B. Browning. A survey of robot learning from demonstration. Robotics and autonomous systems, 57(5):469--483, 2009.
[2]
S. Chernova and A. L. Thomaz. Robot learning from human teachers. Synthesis Lectures on Artificial Intelligence and Machine Learning, 8(3):1--121, 2014.
[3]
L. C. Cobo, C. L. Isbell, and A. L. Thomaz. Object focused q-learning for autonomous agents. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, pages 1061--1068. International Foundation for Autonomous Agents and Multiagent Systems, 2013.
[4]
S. Griffith, K. Subramanian, J. Scholz, C. Isbell, and A. L. Thomaz. Policy shaping: Integrating human feedback with reinforcement learning. In Advances in Neural Information Processing Systems, pages 2625--2633, 2013.
[5]
J. MacGlashan, M. Babes-Vroman, M. desJardins, M. Littman, S. Muresan, S. Squire, S. Tellex, D. Arumugam, and L. Yang. Grounding english commands to reward functions. In Proceedings of Robotics: Science and Systems, Rome, Italy, July 2015.
[6]
R. Maclin, J. Shavlik, L. Torrey, T. Walker, and E. Wild. Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression. In Proceedings of the National Conference on Artificial intelligence, volume 20, page 819. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2005.
[7]
J. Togelius, S. Karakovskiy, and R. Baumgarten. The 2009 mario ai competition. In Evolutionary Computation (CEC), 2010 IEEE Congress on, pages 1--8. IEEE, 2010.
Index Terms
- Object-Focused Advice in Reinforcement Learning
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Information & Contributors
Information
Published In
May 2016
1580 pages
ISBN:9781450342391
- General Chairs:
- Catholijn M. Jonker,
- Stacy Marsella,
- Program Chairs:
- John Thangarajah,
- Karl Tuyls
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- IFAAMAS
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Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 09 May 2016
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- ONR
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AAMAS '16
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AAMAS '16: International Conference on Agents and Multiagent Systems
May 9 - 13, 2016
Singapore, Singapore
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AAMAS '16 Paper Acceptance Rate 137 of 550 submissions, 25%;
Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
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