Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research

Autor: Dohmen, Jan, Röder, Frank, Eppe, Manfred

One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments. Many open source implementations of current RL algorit

Externí odkaz: http://arxiv.org/abs/2401.14488

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Explainable Reinforcement Learning for Longitudinal Control

Autor: Liessner, Roman, Dohmen, Jan, Wiering, Marco, Rocha, Ana Paula, Steels, Luc, van den Herik, Jaap

Publikováno v: Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 874-881
STARTPAGE=874;ENDPAGE=881;TITLE=Proceedings of the 13th International Conference on Agents and Artificial Intelligence

Deep Reinforcement Learning (DRL) has the potential to surpass the existing state of the art in various practical applications. However, as long as learned strategies and performed decisions are difficult to interpret, DRL will not find its way into

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::50431ee009aec0a9ab2a97dc50cbcb20
https://research.rug.nl/en/publications/d455a9ae-5dcc-4d7d-9967-84959e532fb5

Remarks on the Progress of a Jurisprudence of Sport.

Autor: RUSSELL, J. S.¹

Publikováno v: New York Law School Law Review. 2018/2019, Vol. 63 Issue 2, p175-195. 21p.

OPERA WORLDWIDE.

Publikováno v: Opera Now; Jan/Feb2008, Issue 1, p108-131, 23p, 1 Color Photograph