Reinforcement Learning
Semestr: Summer
Range: 2P+2C
Completion:
Credits: 6
Programme type: Master
Study form: Fulltime
Course language: Czech
Summary:
Keywords:
Course syllabus:
1. Motivation (successes, AGI, human feedback, history)
2. Multi-armed bandit problems (stochastic, contextual)
3. Solving MDPs 1: (Bellman equations, Value iteration)
4. Solving MDPs 2: (Contraction, Policy iteration)
5. Temporal difference learning 1: (TD(0), Sarsa, Q-learning)
6. Temporal difference learning 2: (n-step, Double-Q, DQN)
7. Policy gradient methods 1: (Tabular)
8. Policy gradient methods 2: (Variance reduction, Neural)
9. Combining learning and planning (AlphaZero, muZero)
10. Exploration in RL
11. Multi-agent RL (cooperative vs. adversarial)
12. Applications: Advertising, RLHF, Robotics, …
13. Neuro-science and RL
Seminar syllabus:
Literature:
Jako primární materiál budou k dispozici online scripta (ne slidy).
Doporučená literatura:
● Reinforcement Learning, second edition: An Introduction, Richard Sutton, Andrew G. Barto, 2018.
● Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNsto PPO and RLHF, Maxim Lapan, 2020.
● Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions, Warren B. Powel, 2022.