Deep Reinforcement Learning for Learning Machines

  Diesen Termin in den persönlichen Kalender (z.B. Outlook, Thunderbird, Lotus Notes) übernehmen
Startdatum:23. Mai 2017
Startzeit:13:30 Uhr
Stoppzeit:14:30 Uhr
Veranstalter:Fachbereich Informatik
Referent:Martin Riedmiller; Google Deepmind
Ort:S2 02|C205

Recently, artificial intelligence has experienced a big boost of interest in both academia and industry and even the general public. A lot of this is due to the success of deep learning methods, i.e., neural networks with a considerable number of hidden layers, that show amazing results in a variety of domains such as computer vision or speech recognition.

In this talk, I will in particular focus on data-efficient reinforcement learning methods. Applying neural networks in the context of self-learning control of dynamic systems has been our research topic for more than 20 years. I will highlight progress in this area by going through several examples in both simulated and real domains, from Atari playing agents to more recent examples in robotics.




Martin Riedmiller has been the leading researcher at using neural networks during both the so-called 2nd neural winter as well as the recent renaissance with the deep neural networks. After a PhD and Post-Doc at the University of Karlsruhe and a Post-Doc at Carnegie Mellon University, he has been professor at Dortmund, Osnabrück and Freiburg, as well as a visiting professor at Stanford University and University of Southern California. Recently, he surprised all of us by moving to Google Deepmind! At Deepmind, he has created a team of researchers that has delivered some unprecedented und previously un-imaginable results of reinforcement learning, ranging from learning Atari games to world class go playing.




Hochschulstraße 10
64289 Darmstadt

+49 6151 16-25501

Hinweise auf weitere lokale Veranstaltungen des Fachbereichs können an events@informatik... gesendet werden.

A A A | Drucken Drucken | Impressum Impressum | Sitemap Sitemap | Suche Suche | Kontakt Kontakt | Webseitenanalyse: Mehr Informationen
zum Seitenanfangzum Seitenanfang