Intelligent Robot Learning Laboratory (IRL Lab) Agents Teaching Humans and Agents

By: Yusen Zhan and Matthew E. Taylor

We developed a advice model framework to provide theoretical and practical analysis for agents to teach humans and agents in sequential reinforcement learning tasks. The teacher  agents assist the students (humans or agents) with action advice when the teachers observe the students reach some critical states. Assuming the teachers are optimal, the students will follow the action advice to achieve better performance. [1, 2]

[1] [pdf] Yusen Zhan and Matthew E. Taylor. Online Transfer Learning in Reinforcement Learning Domains. In Proceedings of the AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (SDMIA), November 2015.
[Bibtex]
@inproceedings{2015SDMIA-Zhan,
author={Yusen Zhan and Matthew E. Taylor},
title={{Online Transfer Learning in Reinforcement Learning Domains}},
booktitle={{Proceedings of the {AAAI} Fall Symposium on Sequential Decision Making for Intelligent Agents ({SDMIA})}},
month={November},
year={2015},
bib2html_pubtype={Refereed Workshop or Symposium},
abstract={This paper proposes an online transfer framework to capture the interaction among agents and shows that current transfer learning in reinforcement learning is a special case of online transfer. Furthermore, this paper re-characterizes existing agents-teaching-agents methods as online transfer and analyze one such teaching method in three ways. First, the convergence of Q-learning and Sarsa with tabular representation with a finite budget is proven. Second, the convergence of Q-learning and Sarsa with linear function approximation is established. Third, the we show the asymptotic performance cannot be hurt through teaching. Additionally, all theoretical results are empirically validated.}
}
[2] [pdf] Yusen Zhan, Anestis Fachantidis, Ioannis Vlahavas, and Matthew E. Taylor. Agents Teaching Humans in Reinforcement Learning Tasks. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS), May 2014.
[Bibtex]
@inproceedings(2014ALA-Zhan,
author={Yusen Zhan and Anestis Fachantidis and Ioannis Vlahavas and Matthew E. Taylor},
title={{Agents Teaching Humans in Reinforcement Learning Tasks}},
booktitle={{Proceedings of the Adaptive and Learning Agents workshop (at {AAMAS})}},
month={May},
year= {2014},
bib2html_pubtype={Refereed Workshop or Symposium},
bib2html_rescat={Reinforcement Learning},
)

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