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Leveraging Human Guidance for Deep Reinforcement Learning Tasks.
Ruohan
Zhang, Faraz Torabi, Lin Guan, Dana
H. Ballard, and Peter Stone.
In Proceedings of the 28th International
Joint Conference on Artificial Intelligence (IJCAI), August 2019.
[PDF]471.1kB [slides.pdf]1.2MB
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated using imitation learning, where the agent learns to imitate human demonstrated decisions. However, human guidance is not limited to the demonstrations. Other types of guidance could be more suitable for certain tasks and require less human effort. This survey provides a high-level overview of five recent learning frameworks that primarily rely on human guidance other than conventional, step-by-step action demonstrations. We review the motivation, assumption, and implementation of each framework. We then discuss possible future research directions.
@InProceedings{IJCAI19-zhang,
author = {Ruohan Zhang and Faraz Torabi and Lin Guan and Dana H. Ballard and Peter Stone},
title = {Leveraging Human Guidance for Deep Reinforcement Learning Tasks},
booktitle = {Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI)},
location = {Macao, China},
month = {August},
year = {2019},
abstract = {
Reinforcement learning agents can learn to solve sequential decision tasks by
interacting with the environment. Human knowledge of how to solve these tasks
can be incorporated using imitation learning, where the agent learns to
imitate human demonstrated decisions. However, human guidance is not limited
to the demonstrations. Other types of guidance could be more suitable for
certain tasks and require less human effort. This survey provides a high-level
overview of five recent learning frameworks that primarily rely on human
guidance other than conventional, step-by-step action demonstrations. We
review the motivation, assumption, and implementation of each framework. We
then discuss possible future research directions.
},
}
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