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Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks.
Ruohan
Zhang, Faraz Torabi, Garrett
Warnell, and Peter Stone.
Autonomous Agents and Multi-Agent Systems,
35(31), June 2021.
official online version
A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is the artificial agent that learns and acts, it is still up to humans to specify the particular task to be performed. Classical task-specification approaches typically involve humans providing stationary reward functions or explicit demonstrations of the desired tasks. However, there has recently been a great deal of research energy invested in exploring alternative ways in which humans may guide learning agents that may, e.g., be more suitable for certain tasks or require less human effort. This survey provides a high-level overview of five recent machine learning frameworks that primarily rely on human guidance apart from pre-specified reward functions or conventional, step-by-step action demonstrations. We review the motivation, assumptions, and implementation of each framework, and we discuss possible future research directions.
@article{JAAMAS21-Zhang,
author={Ruohan Zhang and Faraz Torabi and Garrett Warnell and Peter Stone},
title={Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks},
journal={Autonomous Agents and Multi-Agent Systems},
doi={10.1007/s10458-021-09514-w},
month="June",
year="2021",
volume="35",
number="31",
abstract = {A longstanding goal of artificial intelligence is to create artificial agents capable of learning to perform tasks that require sequential decision making. Importantly, while it is the artificial agent that learns and acts, it is still up to humans to specify the particular task to be performed. Classical task-specification approaches typically involve humans providing stationary reward functions or explicit demonstrations of the desired tasks. However, there has recently been a great deal of research energy invested in exploring alternative ways in which humans may guide learning agents that may, e.g., be more suitable for certain tasks or require less human effort. This survey provides a high-level overview of five recent machine learning frameworks that primarily rely on human guidance apart from pre-specified reward functions or conventional, step-by-step action demonstrations. We review the motivation, assumptions, and implementation of each framework, and we discuss possible future research directions.},
wwwnote = {<a href="https://link.springer.com/article/10.1007/s10458-021-09514-w">official online version</a>},
}
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