Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Herke van Hoof"'
Autor:
Nutan Chen, Walterio W. Mayol-Cuevas, Maximilian Karl, Elie Aljalbout, Andy Zeng, Aurelio Cortese, Wolfram Burgard, Herke van Hoof
Publikováno v:
Frontiers in Robotics and AI, Vol 10 (2024)
Externí odkaz:
https://doaj.org/article/cff2c5a1bc9b45249f7fbe7856bec7b8
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
In robotic tasks, we encounter the unique strengths of (1) reinforcement learning (RL) that can handle high-dimensional observations as well as unknown, complex dynamics and (2) planning that can handle sparse and delayed rewards given a dynamics mod
Hex is a turn-based two-player connection game with a high branching factor, making the game arbitrarily complex with increasing board sizes. As such, top-performing algorithms for playing Hex rely on accurate evaluation of board positions using neur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67a86fa49bed72dc8093b253117bc1a7
Publikováno v:
Integration of Constraint Programming, Artificial Intelligence, and Operations Research ISBN: 9783031080104
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::570a1e881313522f6d7b7c0c029667e5
https://doi.org/10.1007/978-3-031-08011-1_14
https://doi.org/10.1007/978-3-031-08011-1_14
Publikováno v:
ICRA
2021 IEEE International Conference on Robotics and Automation (ICRA 2021): May 31-June 4, 2021, Xi'an, China, 10682-10688
STARTPAGE=10682;ENDPAGE=10688;TITLE=2021 IEEE International Conference on Robotics and Automation (ICRA 2021)
2021 IEEE International Conference on Robotics and Automation (ICRA 2021): May 31-June 4, 2021, Xi'an, China, 10682-10688
STARTPAGE=10682;ENDPAGE=10688;TITLE=2021 IEEE International Conference on Robotics and Automation (ICRA 2021)
Solving robotic navigation tasks via reinforcement learning (RL) is challenging due to their sparse reward and long decision horizon nature. However, in many navigation tasks, high-level (HL) task representations, like a rough floor plan, are availab
Publikováno v:
RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020
RECSYS 2020
RecSys
RECSYS 2020
RecSys
Reinforcement learning for recommendation (RL4Rec) methods are increasingly receiving attention as an effective way to improve long-term user engagement. However, applying RL4Rec online comes with risks: exploration may lead to periods of detrimental
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a89e38aaa26520e04396d99b825fd0fc
https://dare.uva.nl/personal/pure/en/publications/keeping-dataset-biases-out-of-the-simulation(c902af09-f699-4e40-86b2-f317eed290d4).html
https://dare.uva.nl/personal/pure/en/publications/keeping-dataset-biases-out-of-the-simulation(c902af09-f699-4e40-86b2-f317eed290d4).html
Publikováno v:
Machine Learning. 106:1705-1724
To learn control policies in unknown environments, learning agents need to explore by trying actions deemed suboptimal. In prior work, such exploration is performed by either perturbing the actions at each time-step independently, or by perturbing po
Publikováno v:
2019 International Conference on Robotics and Automation (ICRA): Montreal, Quebec, Canada, 20-24 May 2019, 1, 768-774
ICRA
ICRA
Diversity of environments is a key challenge that causes learned robotic controllers to fail due to the discrepancies between the training and evaluation conditions. Training from demonstrations in various conditions can mitigate---but not completely
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f09b17e365ba24e89cf988d14744e308
http://arxiv.org/abs/1903.05697
http://arxiv.org/abs/1903.05697
Publikováno v:
IROS
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Macau, China, 3-8 November 2019, 5034-5040
STARTPAGE=5034;ENDPAGE=5040;TITLE=2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Macau, China, 3-8 November 2019, 5034-5040
STARTPAGE=5034;ENDPAGE=5040;TITLE=2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98d902252a8380710ffbc51cf91db08b
https://doi.org/10.1109/IROS40897.2019.8968535
https://doi.org/10.1109/IROS40897.2019.8968535
Publikováno v:
Machine Learning. 104:337-357
Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims