# 401-3910-71L Student Seminar on Reinforcement Learning

Semester | Autumn Semester 2021 |

Lecturers | M. Schweizer |

Periodicity | non-recurring course |

Language of instruction | English |

Comment | Number of participants limited to 12. |

### Courses

Number | Title | Hours | Lecturers | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

401-3910-71 S | Student Seminar on Reinforcement Learning | 2 hrs |
| M. Schweizer |

### Catalogue data

Abstract | The aim of this seminar is to give an introduction to some of the mathematical ideas behind reinforcement learning. This includes stochastic optimisation and convergence analysis. The emphasis is on mathematical theory, not on developing and testing algorithms. |

Objective | The aim of this seminar is to give an introduction to some of the mathematical ideas behind reinforcement learning. This includes stochastic optimisation and convergence analysis. The emphasis is on mathematical theory, not on developing and testing algorithms. |

Content | The aim of this seminar is to give an introduction to some of the mathematical ideas behind reinforcement learning. This includes stochastic optimisation and convergence analysis. The emphasis is on mathematical theory, not on developing and testing algorithms. |

Literature | See the seminar homepage at Link |

Prerequisites / Notice | The underlying textbook mostly works with stochastic control problems for discrete-time Markov chains with a finite state space. But for a proper understanding, students should be familiar with measure-theoretic probability theory as well as stochastic processes in discrete time, and in particular with the construction of Markov chains on the canonical path space via the Ionescu-Tulcea theorem. |

### Performance assessment

Performance assessment information (valid until the course unit is held again) | |

Performance assessment as a semester course | |

ECTS credits | 4 credits |

Examiners | M. Schweizer |

Type | ungraded semester performance |

Language of examination | English |

Repetition | Repetition only possible after re-enrolling for the course unit. |

### Learning materials

Main link | Seminar on Reinforcement Learning |

Only public learning materials are listed. |

### Groups

No information on groups available. |

### Restrictions

Places | Limited number of places. Special selection procedure. |

Beginning of registration period | Registration possible from 02.08.2021 |

Priority | Registration for the course unit is until 29.08.2021 only possible for the primary target group |

Primary target group | Mathematics MSc (437000)
Applied Mathematics MSc (437100) |

Waiting list | until 27.09.2021 |

End of registration period | Registration only possible until 17.09.2021 |

### Offered in

Programme | Section | Type | |
---|---|---|---|

Mathematics Master | Seminars | W |