Stage-oe-small.jpg

Thema4536

Aus Aifbportal
Version vom 27. November 2019, 13:06 Uhr von Dh1659 (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Abschlussarbeit |Titel=Efficient Training Self Driving Cars using Reinforcement Learning |Abschlussarbeitstyp=Bachelor, Master |Betreuer=Mohammd Karam Daabou…“)
(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu:Navigation, Suche



Efficient Training Self Driving Cars using Reinforcement Learning




Informationen zur Arbeit

Abschlussarbeitstyp: Bachelor, Master
Betreuer: Mohammd Karam Daaboul
Forschungsgruppe: Angewandte Technisch-Kognitive Systeme
Partner: FZI Forschungszentrum Informatik
Archivierungsnummer: 4536
Abschlussarbeitsstatus: Offen
Beginn: 27. November 2019
Abgabe: unbekannt

Weitere Informationen

Reinforcement learning has achieved remarkable results in areas such as simulated robotics or at playing Atari computer games. As reinforcement learning agents learn through exploration by trial-and-error, training in the real world would result in undesirable driving actions leading to possible damage to the system and its environment. To train a reinforcement learning agent using functional approximations such as the neural network, the agent interacts with the world, the world returns a good reward if the action used was good or negative, if not. This reward is used to train the network. During training, the loss function should converge to zero and the reward function must increase. A bad choice of hyperparameters such as the shape of the network can lead to poor results. Therefore we may have to tune the hyperparameters several times to get a better result. Every time we tune the parameters, we have to restart the training. A big challenge in training an agent in the real world is learning from limited samples. Almost all of these real systems are either slow, fragile or so expensive that the data they generate is expensive, and policy learning must be data efficient. Off-policy reinforcement learning aims to leverage experience collected from prior policies for sample-efficient learning.

WE OFFER

  • an interdisciplinary research environment with partners from science and industry
  • constructive cooperation with bright, motivated employees
  • a comfortable working atmosphere

WE EXPECT

  • Knowledge in the field of artificial intelligence and Machine Learning
  • Ability to implement both state of the art and experimental algorithms
  • Good Python knowledge
  • High creativity and productivity
  • Experience with Reinforcement Learning is an advantage

REQUIRED DOCUMENTS

  • current grade report
  • CV

CONTACT

Mohammd Karam Daaboul