Projektgruppe 649 (Sommersemester 2021)

• Veranstalter: Prof. Dr. Günter Rudolph M.Sc. Marco Pleines M.A. Nicolas Fischöder

Modulhandbuch

PG Richtlinien

Veranstaltungsort: Otto-Hahn-Straße 14, Raum 204 (Pool des Lehrstuhl 11).

Veranstaltungsort: Otto-Hahn-Straße 14, Raum 204 (Pool des Lehrstuhl 11).

Veranstaltungsort: Otto-Hahn-Straße 14, Raum 204 (Pool des Lehrstuhl 11).

• R. S. Sutton and A. G. Barto : “Reinforcement Learning: An Introduction”, MIT Press, Cambridge, 2018 (ISBN: 9780262039246)
• G. N. Yannakakis and J. Togelius : “Artificial Intelligence and Games”, Springer, 2018 (ISBN: 9783319635194)
• M. Lapan: “Deep Reinforcement Learning Hands-On”, Packt Publishing, 2018 (ISBN: 9781788834247)
• L. Graesser and W. L. Keng: “Foundations of Deep Reinforcement Learning”, Addison-Wesley Professional, 2019 (ISBN: 9780135172490)
• M. Morales: “grokking Deep Reinforcement Learning”, Manning Publications Co., 2020 (ISBN: 9781617295454)

• V. Mnih et al., “Human-level control through deep reinforcement learning”, Nat., vol. 518, no. 7540, pp. 529-533, 2015 K.Cobbe et al., “Leveraging procedural generation to benchmark reinforcement learning”, CoRR,vol. abs/1912.01588, 2019.
• A. Juliani et al., “Obstacle tower: A generalization challenge in vision, control, and planning”, in Proceedings IJCAI 2019, Macao, China (S. Kraus, ed.), pp. 2684-2691, ijcai.org, 2019.
• O. Vinyals et al., “Grandmaster level in starcraft II using multi-agent reinforcement learning”, Nat., vol. 575, no. 7782, pp. 350-354, 2019.
• C. Berner et al., “Dota 2 with large scale deep reinforcement learning”, CoRR, vol. abs/ 1912.06680, 2019.
• C. Hidber, “Reinforcement Learning: a gentle Introduction and industrial Application”, aufgerufen über https://youtu.b/3RjSanoNIlk am 14.12.2020, 2019.
• M. Andrychowicz et al., “Learning dexterous in-hand manipulation”, Int. J. Robotics Res., vol. 39, no. 1, 2020.
• M. G. Bellemare et al., “Autonomous navigation of stratospheric balloons using reinforcement learning”, Nat. vol. 588, pp.77-82, 2020.