Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Brunnbauer, Axel"'
Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents. Existing methods often depend on online interactions or access to system dynamics, limiting their pr
Externí odkaz:
http://arxiv.org/abs/2410.17898
The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vit
Externí odkaz:
http://arxiv.org/abs/2403.17805
Autor:
Brunnbauer, Axel, Berducci, Luigi, Brandstätter, Andreas, Lechner, Mathias, Hasani, Ramin, Rus, Daniela, Grosu, Radu
World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dimensional observations (e.g., pixel inputs) has become practicable on s
Externí odkaz:
http://arxiv.org/abs/2103.04909
Autor:
Brunnbauer, Axel
Reinforcement learning (RL) is currently one of the most active machine learning research fields. RL algorithms have been successfully deployed ubiquitously in many real-world application domains, such as autonomous vehicles, intelligent production s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::72288ed4417c78315d2935819b3c922f
Autor:
Brunnbauer, Axel, Bader, Markus
Proceedings of the Joint ARW & OAGM Workshop 2019
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d02c8148bab79a29ca3d075a895488f6
