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DTSTART:20200812T030000
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SUMMARY;CHARSET=UTF-8:Summer Academy Session: Deep Reinforcement Learning
URL:https://www.sfg.at/e/summer-academy-session-deep-reinforcement-learning
 /
DESCRIPTION;CHARSET=UTF-8:Deep Reinforcement Learning\nRecent advances and 
 successes of Deep Reinforcement Learning have clearly shown the remarkable
  potential that lies within this compelling technique.\n\nDeep Reinforceme
 nt Learning\n\n12.08.2020 15:00 &#8211\; 17:00 | Language: English\n\n\nTa
 rget Group:\nPeople who are interested in the topic\, especially people wi
 th an industrial background (all domains)\nAbstract:\nRecent advances and 
 successes of Deep Reinforcement Learning have clearly shown the remarkable
  potential that lies within this compelling technique. DRL systems can be 
 deployed across a broad variety of domains\, such as robotics\, autonomous
  driving or flying\, chess\, go or poker\, in production facilities and in
  finance\, in control theory and in optimization\, and even in mathematics
 . This list could be extended to almost arbitrary length. But what is it t
 hat makes DRL successful in all those very different applications? The ans
 wer to that question is that DRL systems are designed to be generic learni
 ng  schemes that are – at least in principle – not limited in what ta
 sk they learn. However\, there are also a variety of issues one has to dea
 l with when deploying DRL agents to solve problems. For example\, one need
 s simulation environments to train the systems that capture all relevant a
 spects of the real world system the trained agent is immersed in after lea
 rning. Sometimes it can also be particularly hard for a DRL agent to learn
  a certain task. There are several other technicalities that have to be ma
 stered carefully in order to produce a successful agent. Nonetheless DRL i
 s currently one of the most promising\, interesting and exciting fields of
  machine learning. Exploring DRL applications now can mean having the uppe
 r hand in key technologies in a few years. Want to learn more and discuss 
 with us? Then join our session! We are really excited to meeting you there
 !\nAfter the event you will know:\n\n\n\nWhat Deep Reinforcement Learning 
 is and how it works\n\n\n\n\n\n\nSuccessful DRL applications across variou
 s domains\n\n\n\n\n\n\nThe current state of the art: Where does it shine\n
 \n\n\n\n\n\nWhat are current problems/issues of DRL\n\n\n\n\n\n\nWhat to e
 xpect from DRL\n\n\n\n\n\n\nHow you could deploy DRL in your field\n\n\n\n
 \nSpeaker\n\n\n\n\n\nAndreas Windisch\nKnowledge Discovery\n\n\n\n\n
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