Speaker
Joel Axel Wulff
Description
Proposal: Reinforcement Learning for Particle Accelerator control: A real-world example
Hour 1: Introduction to Reinforcement Learning for Particle Accelerators
- Basic Concepts:
- Overview of Reinforcement Learning (RL) fundamentals.
- Definitions and distinctions:
- Model-free vs. model-based.
- Off-policy vs. on-policy approaches.
- Applications and Considerations:
- Discussion of problem types and environmental variables affecting model selection in practical scenarios.
- Analysis of drawbacks and benefits of different RL architectures.
- Practical examples:
- Real-world examples of RL in particle accelerators (e.g., CERN).
- Case study introduction: Optimization of RF triple splittings in the Proton Synchrotron (PS).
Hour 2: Optimizing RF Triple Splittings with Reinforcement Learning
- Problem Definition:
- Explanation of PS RF operations and the triple splitting optimization challenge for LHC-type beams.
- Overview of the physics and parameters involved in optimization.
- Optimization Approach:
- Justification for choosing RL and specific RL architectures.
- Step-by-step walkthrough:
- Initial simulations and trials.
- Challenges and lessons learned.
- Final operational solution deployed in the control room.
Exercise Session: Training RL Agents for RF Optimization (1 hour)
- Objective:
- Train RL agents to optimize RF double splitting settings in simulation for improved beam quality.
- Implementation:
- Use SWAN notebooks with provided skeleton code.
- Define a custom gymnasium environment for the double splitting problem, given:
- Pre-implemented simulation data loaders.
- Basic loss function for optimization.
| Number of lecture hours | 2 |
|---|---|
| Number of exercise hours | 1 |
| Attended school | tCSC 2024 (Split) |
Author
Joel Axel Wulff
