Written Exam CHANGED: 9:00 – 11:00 Thursday 21.7, room V7.11
Instructors: Vien Ngo and Hung Ngo
Machine Learning & Robotics Lab, IPVS, University of Stuttgart.
Time:
 Lessons: Wed. 14:0015:30 at room 0.108
 Tutorials: Tue. 17:3019:00 at room 38.03
Description
Reinforcement learning is an area of machine learning in computer science, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulationbased optimization, statistics, and genetic algorithms. (Wikipedia)
As encountered in nature, human or animal can learn by interacting with environment, and see the effect to adapt and respond to changes to achieve the goals. These creatures easily perceive cause and effect without having supervised data or teachers. Reinforcement learning is a type of machine learning frameworks which addresses such learning machinery. It aims to find an optimal policy to achieve the goal by interacting with the environment in absence of explicit teachers. For instance, the recent success of AlphaGO which is a computer program to play the game GO; in robotics, robots learn to understand environment and achieve new skills; in commercial smart ads where ads are optimally adaptedtouser chosen by learning through activities of users on the site; etc..
Schedule, slides & exercises
date  topics  slides  exercises 

06.04  Introduction  01Introduction  No tutorial on 05.06 
13.04  MDP, Dynamic Programming  02MDP  01exercise (due 12.04) 01solution 
20.04  TD Learning  03TDLearning  02exercise (due 19.04) 16RLcode 
26.04  QLearning  (cont.)  03exercise (due 26.04) 
03.05  RL with Func. Approx.  04FunctionApproximation  04exercise (due 03.05) 
10.05  Deep RL  (cont.)  05exercise (due 10.05) 
17.05  Semester Break  No Lectures  
24.05  Integrated LearningPlanning  05ModelRLDynaExplore  06exercise (due 24.05) 
31.05  ExplorationExploitation  (cont.)  07exerciserevised (due 31.05) 
08.06  Policy Search  06PolicySearch  08exercise (due 07.06) 
15.06  (cont.)  09exercise (due 14.06) e09code 

22.06  (cont.)  10exercise (due 21.06) 

29.06  Hierarchical RL  07HierarchicalRL  11exercise (due 28.06) 
06.07  Inverse RL  08InverseRL  12exercise (due 05.07) 
13.07  Review  Q&A lecture  13exercise (due 12.07) EXERCISE Canceled (Vien's Travel) 
Literature
 R. Sutton and A. Barto, Reinforcement Learning, 1998. This book is freely available online
 C. Szepesvari, Algorithms for Reinforcement Learning, 2010. Draft version is freely available online.
 Steven M. LaValle, Planning Algorithms, 2006, Cambridge University Press. This book is freely available online.