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Imitation Learning

Module Description

Lecturer	Artem Sokolov
Module Type	Hauptseminar
Language	English
First Session	17.02.2020
Last Session	21.02.2020
Time and Place	daily, 10:00-16:00, INF 327 / SR 3
End of Commitment Period	21.02.2020

Prerequisite for Participation

• Good Knowledge of Probability Theory
• Knowledge of the following will be helpful: foundations of statistical machine Learning, reinforcement learning and neural networks

Assessment

• Regular attendance and active participation
• Presentation or Implementation project

Content

This module provides an introduction into theory and practice of learning from demonstrations with a focus on natural language processing use-cases. Closely related to structured prediction and reinforcement learning, imitation learning is particularly suited for sequence prediction tasks, where often good success metrics or intermediate rewards are hard to define, while in the same time it is easy to provide demonstrations of correct behavior. After taking this module you will be able to formulate imitation learning problems, understand deficiencies of some straight-forward approaches to it, map structured prediction tasks to imitation learning, and solve them using deep learning techniques.

Module Overview

Agenda

see material page

Literature

• Sutton and Barto "Reinforcement Learning" (2018) 2nd edition. MIT Press.
  http://incompleteideas.net/book/the-book-2nd.html
• Szepesvari (2010). Algorithms for Reinforcement Learning. Morgan & Claypool.
  https://sites.ualberta.ca/~szepesva/RLBook.html
• Osa, Pajarinen, Neumann, Bagnell, Abbeel, Peters (2018), An Algorithmic Perspective on Imitation Learning. Foundations and Trends in Robotics.
  https://arxiv.org/abs/1811.06711
• Daumé III, A Course in Machine Learning, Chapter 18
  http://ciml.info/
• Goldberg (2015). A Primer on Neural Network Models for Natural Language Processing.
  https://arxiv.org/abs/1510.00726
• Neubig (2017). Neural Machine Translation and Sequence-to-sequence Models: A Tutorial.
  https://arxiv.org/abs/1703.01619

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