Reinforcement Learning and Decision Making Under Uncertainty

The course will give a thorough introduction to reinforcement learning. The first 8 weeks will be devoted to the core theory and algorithms of reinforcement learning. The final 6 weeks will be focused on project work, during which more advanced topics will be inroduced.

The first 6 weeks will require the students to complete 3 assignments. The remainder of the term, the students will have to prepare a project, for which they will need to submit a report.


  1. Beliefs and Decisions
    Bayes Decision and Game Theory
  2. Bayesian Analysis
    Estimation Theory and Concentration Inequalities
  3. Bandit problems
    MDPs and belief states
  4. MDP Theory: Value Iteration
    Policy Iteration
  5. Temporal Differences
    Modified Policy Iteration
  6. Sarsa / Q-Learning
    Stochastic Approximation / Actor-Critic
  7. Function Approximation, Gradient Methods
    Large-Scale RL
  8. Bayesian RL
  9. UCB, Regret bounds
    UCRL, Regret bounds
  10. Monte Carlo Planning: UCT/MCTS/AlphaZero
  11. Advanced Bayesian models
  12. Inverse Reinforcement Learning
  13. Multiagent extensions: Bayesian Games
  14. Group work


Code 62111
Type Course
Site Neuchâtel
Track(s) T6 – Data Science
Semester S2022


Lecturer(s) Christos Dimitrakakis
Language english
Course Page

The course page in ILIAS can be found at

Schedules and Rooms

Period Weekly
Schedule Friday, 14:15 - 18:00
Location UniNE, Unimail
Room B217

Additional information


First Lecture
The first lecture will be announced later.