winter semester 2010/2011, Friday, 10.15 s.t., room ND 6/99
The lecture provides an introduction to reinforcement learning (RL). It covers the basics of RL, including Markov decision processes, dynamic programming, and temporal-difference learning. Additionally, special topics such as policy gradient methods as well as the connection evolutionary computation are presented.
Very basic knowledge in statistics and analysis is required, basic knowledge about neural networks is helpful but not necessary.
The second written exam (Nachklausur) will take place in NA 2/99, Mo 10.08.2011 at 9.00-11.00 o'clock.
The results of the exam are finally there. I apologize for the long delay.
Correcting them will take a bit longer than usual because they will be send to France for me to correct.)
The slides of each lecture unit will be available here. Topics Unit 1: Introduction Unit 2: The reinforcement learning problem Unit 3: Dynamic programming Unit 4: Monte Carlo methods Unit 5: Temporal difference learning Unit 6: Eligibility traces Mini-Unit: Actor-critic methods Unit 7: Function approximation Unit 8: Planning and learning Unit 9: Least-squares temporal difference learning (LSTD) Unit 10: Policy gradient methods Unit 11: Evolutionary reinforcement learning Outlook: Other reinforcement learning areas This slides and topics will be updated after each lecture.
Exercises due on 29.10.2010 Exercises due on 12.11.2010 Exercises due on 03.12.2010 Exercises due on 14.01.2011 Exercises due on 04.02.2011 The last exercises due on 04.02.2011 will be available at 28.01.2011.
Based on a fresh install of Ubuntu 10.10 the following packages are needed (install from "System->Systemadministration->Synoptic package manager"): - binary install (.deb): g++ - source install: cmake and g++
Verena Heidrich-Meisner room: from 01.11.2010 NB 3/26 telefon: 0234-32 27974 email: verena.heidrich-meisner at rub.de