About
Supervised and unsupervised machine learning has shown great performance in finding mappings between probability distributions, as e.g. in classification problems or for artificial data generation. A more difficult class of problems is decision-making, e.g. controlling dynamical systems or building mathematical algorithms because the framework requires additional time-ordering. Reinforcement learning (RL) was successful in solving such problems, e.g. in finding strategies for games, optimizing algorithms for high-performance computing, and controlling magnetic fields for nuclear fusion reactors and particle accelerators. In this lecture, I will provide an introduction to the framework, with pedagogical examples, mathematical details, and applications in particle physics. In detail, I will cover: 1) Markov decision processes (MDPs) as the mathematical foundation of RL; 2) Solving small MPDs with tabular methods; 3) Solving large MDPs with policy gradient methods..
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The talk
Credits and licence
- Institution
- CERN
- Department
- Inverted CSC
- Speaker
- Felix Wagner
- Year
- 2023
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed