Learning Options From Demonstrations: APac-ManCase Study

IEEE Transactions on Games, pp. 91-96, 2018.

Cited by: 0|Bibtex|Views4|DOI:https://doi.org/10.1109/TCIAIG.2017.2658659
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Abstract:

Reinforcement learning (RL) is a machine learning paradigm behind many successes in games, robotics, and control applications. RL agents improve through trial-and-error, therefore undergoing a learning phase during which they perform suboptimally. Research effort has been put into optimizing behavior during this period, to reduce its dura...More

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