Ruprecht-Karls-Universität Heidelberg
Bilder vom Neuenheimer Feld, Heidelberg und der Universität Heidelberg

Reinforcement Learning

Kursbeschreibung

Studiengang Modulkürzel Leistungs-
bewertung
BA-2010 AS-CL 8 LP
Master SS-CL, SS-TAC 8 LP
Dozenten/-innen Stefan Riezler
Veranstaltungsart Hauptseminar
Erster Termin 24.10.2017
Zeit und Ort Di, 11:1512:45, INF 325 / SR 24 (SR)
Commitment-Frist tbd.

Teilnahmevoraussetzungen

Master: Grundlagen der Wahrscheinlichkeitstheorie, Statistik und Linearen Algebra
Bachelor: Erfolgreicher Abschluss der Kurse "Formal Foundations of Computational Linguistics: Mathematical Foundations " und "Statistical Methods for Computational Linguistics"

Leistungsnachweis

  • Aktive und regelmässige Teilnahme
  • Referat inklusive Vorbereitung von Diskussionsfragen
  • Implementierungsprojekt oder Abschlussarbeit

Inhalt

Reinforcement learning is a machine learning technique that is placed between supervised and unsupervised learning. Instead of learning from explicit supervision by ground-truth examples, an input-output relation is learned trough interaction of a system with the environment or user. Learning from implicit feedback such as rewards that evaluate the quality of predicted outputs is less costly than explicit supervision and allows to learn in uncharted territory. The goal of this class is to introduce into the central theoretical and algorithmical concepts of reinforcement learning, with a special focus on applications to structured predcition problems in natural language processing.

Possible topis of the class are:

  • Markov Decision Processes vs. Multi-Armed Bandits
  • Exploration vs. Exploitation
  • Prediction vs. Control
  • On-Policy vs. Off-Policy Learning
  • Dynamic Programming vs. Monte Carlo vs. Temporal Difference Learning
  • Critic-Only vs. Actor-Only vs. Actor-Critic Algorithms

Kursübersicht

Seminarplan

Datum Sitzung Materialien

Literatur

  • Sutton & Barto (1998). Reinforcement Learning. An Introduction. MIT Press.
  • Szepesvari (2010). Algorithms for Reinforcement Learning. Morgan & Claypool.

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