Teacher(s)
Language
French
Content
Part 1: Foundations of probabilities, system, and optimal control
Part 2: Exact algorithms for optimal decision-making and control
Part 3: Approximate algorithms
Part 4: Data-driven optimal decision-making and control, and applications
Part 2: Exact algorithms for optimal decision-making and control
Part 3: Approximate algorithms
Part 4: Data-driven optimal decision-making and control, and applications
Teaching methods
Learning will be based on face-to-face courses, interlaced with a homework-based practical project and exercise sessions.
Evaluation methods
The evaluation will be based on:
- Homework exercises of project-type nature
- An oral exam, during which the homework will be discussed, possibly out of the official exam session.
- Homework exercises of project-type nature
- An oral exam, during which the homework will be discussed, possibly out of the official exam session.
Other information
Note that the focus of this course will be on tools from stochastic optimal control, which will be applied to problems in finance in the homeworks and projects.
Online resources
Bibliography
Meyn, Control Systems and Reinforcement Learning (Cambridge University Press, 2022)
Faculty or entity
