Rapidly exploring learning trees

Rapidly exploring learning trees

Inverse Reinforcement Learning (IRL) for path planning enables robots to learn cost functions for difficult tasks from demonstration, instead of hard-coding them. However, IRL methods face practical limitations that stem from the need to repeat costly planning procedures. In this paper, we propose R...

書誌詳細
主要な著者:	Shiarlis, K, Messias, J, Whiteson, S
フォーマット:	Conference item
出版事項:	IEEE 2017

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