Games and reinforcement learning

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

The field of reinforcement learning is very hot recently, because of advances in deep learning, recurrent neural networks and general artificial intelligence. This course is intended to give you both theoretical and practical background so you can participate in related research activities.

Presented in English.

Requirements:

BI-ZUM - Introduction to artificial intelligence

Syllabus of lectures:

Algorithmic game theory

1. Sealed-bid combinatorial auctions

2. Iterative combinatorial auctions

3. Stable matching

4. Congestion games. Selfish routing and the price of anarchy

5. Potential games. Network cost-sharing games

6. Best response dynamics. No-regret dynamics.

Introduction to Reinforcement Learning

7. Multiarmed Bandit Algorithms.

8. Finite Markov Decision Processes

9. Dynamic Programming

10. Montecarlo methods

11. Temporal-Difference learning

12. Multi-step bootstrapping

13. Planning and learning with tabular methods

Syllabus of tutorials:

Algorithmic game theory

1. Mechanism design basics. Auctions of physical goods.

2. Sponsored search auctions (online advertising).

3. Congestion games. Selfish routing and the price of anarchy

4. Traffic assignment in networks.

5. Best response dynamics. No-regret dynamics.

6. Rock, paper, scissors.

Introduction to Reinforcement Learning

7. Multiarmed Bandit Algorithms.

8. Markov chains and MDP's.

9. Algorithms: Q-learning, TD

10. Playing tic-tac-toe, checkers.

11. Tensorflow introduction.

12. Case studies: TD-gammon, Atari games, Go playing.

13. OpenAI Gym. Policy gradient algorithm.

Study Objective:

Teach theoretical and practical aspects of the game theory and reinforcement learning.

Study materials:

Reinforcement Learning: An introduction, Sutton and Barto, 2nd edition draft, 2017.

Algorithmic Game Theory, Roughgarden, Tardos, Vazirani and Nisan, 2007.

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Bc. Branch Security and Information Technology, in Czech, Version 2015 to 2020 (elective course)
• Knowledge Engineering, in Czech, Presented in Czech, for Students who Enrolled in 2015 (elective course)
• Master Informatics, Presented in Czech, Version for Students who Enrolled in 2015 (elective course)
• Knowledge Engineering, in Czech, Presented in Czech, Version 2016 and and 2017 (elective course)
• Computer Security, Presented in Czech, Version 2016 to 2019 (elective course)
• Computer Systems and Networks, Presented in Czech, Version 2016 to 2019 (elective course)
• Design and Programming of Embedded Systems, in Czech, Version 2016 to 2019 (elective course)
• Specialization Web and Software Engineering, in Czech, Version 2016 to 2019 (elective course)
• Specialization Software Engineering, in Czech, Version 2016 to 2019 (elective course)
• Specialization Web Engineering, Presented in Czech, Version 2016 to 2019 (elective course)
• Master Informatics, Presented in Czech, Version 2016 to 2019 (elective course)
• Specialization System Programming, Presented in Czech, Version 2016 to 2019 (elective course)
• Specialization Computer Science, Presented in Czech, Version 2016-2017 (elective course)
• Computer Security, Presented in English, Version 2016 až 2020 (elective course)
• Computer Systems and Networks, Presented in English, Version 2016 až 2020 (elective course)
• Design and Programming of Embedded Systems, in English, Version 2016 až 2020 (elective course)
• Specialization Software Engineering, in English, Version 2016 až 2020 (elective course)
• Specialization Computer Science, Presented in Czech, Version 2018 to 2019 (elective course)
• Knowledge Engineering, in Czech, Presented in Czech, Version 2018 to 2019 (elective course)