2nd collaboration workshop on Reinforcement Learning for Autonomous Accelerators (RL4AA'24)

Session

Student Session

Feb 6, 2024, 9:10 AM

Blue lecture hall (Universität Salzburg (Paris-Lodron-Universität))

45. Exploring the Dynamics of Reinforcement Learning in Aerospace Control

Georg Schäfer (Josef Ressel Centre for Intelligent and Secure Industrial Automation)

2/6/24, 9:10 AM

Student Talk

The Quanser Aero2 system is an advanced laboratory experiment designed for exploring aerospace control systems, featuring two motor-driven fans on a pivot beam for precise control. Its capability to lock axes individually offers both single degree of freedom (DOF) and two DOF operation. The system’s non-linear characteristics and adaptability to multivariable configurations make it especially...

44. Utilizing Machine Learning-optimized Piecewise Polynomial Models in Mechatronics

Mr Hannes Waclawek (osef Ressel Centre for Intelligent and Secure Industrial Automation)

2/6/24, 9:30 AM

Student Talk

The success and fast pace of Machine Learning (ML) in the past decade was also
enabled by modern gradient descent optimizers embedded into ML frameworks such
as TensorFlow. In the context of a doctoral research project, we investigate how
these optimizers can be utilized directly, outside of the scope of neural
networks. This approach holds the potential of optimizing explainable models
with...

69. Robustly Safe Bayesian Optimization

Jannis Lübsen (TUHH/DESY)

2/6/24, 9:50 AM

Student Talk

Safety guarantees for Gaussian processes require the assumption that the true hyperparameters are known. However, this assumption usually does not hold in practice. In this talk, a method is introduced to overcome this issue which estimates confidence intervals of hyperparameters from their posterior distributions. Finally, it can be shown that via appropriate scaling safeness can be robustly...

63. Comparing Q-Learning of Single-Agent and Multi-Agent Reinforcement Learning

Sabrina Pochaba

2/6/24, 10:10 AM

Student Talk

Reinforcement Learning (RL) is a rising subject of Machine Learning (ML). Especially Multi-Agent RL
(MARL), where more than one agent interacts with an environment by learning to solve a task, can model
many real-world problems. Unfortunately, the Multi-Agent case yields more difficulties in the already chal-
lenging field of Reinforcement Learning, like scalability issues, non-stationarity or...

41. Optimising non-linear Injection using Reinforcement Learning

Alexander Schütt (Helmholtz-Zentrum Berlin)

2/7/24, 9:00 AM

Student Talk

Synchrotron light source storage rings aim to maintain a continuous beam current without observable beam motion during injection. One element that paves the way to this target is the non-linear kicker (NLK). The field distribution it generates poses challenges for optimising the topping-up operation.

Within this study, a reinforcement learning agent was developed and trained to optimise the...

42. Enhancing Autonomy of Unmanned Surface Vehicles through Integrated Perception and Control

Juan Montoya Bayardo

2/7/24, 9:20 AM

Student Talk

The Sonobot Unmanned Surface Vehicle (USV), developed by EvoLogics, is a system platform tailored for hydrographic surveying in inland waters. Despite its integrated GPS and autopilot system for autonomous mission execution, the Sonobot lacks a collision avoidance system, necessitating constant operator monitoring and significantly limiting its autonomy.

Recognizing the untapped potential of...

52. Applying Reinforcement Learning to IFMIF-DONES HVAC optimisation

Antonio Manjavacas Lucas (University of Granada)

2/7/24, 9:40 AM

Student Talk

As a critical radiological facility, the International Fusion Materials Irradiation Facility - DEMO Oriented Neutron Source (IFMIF-DONES) will implement effective measures to ensure the safety of its personnel and the environment. To enable the proper implementation of these measures, the ISO 17873 standard has been adopted throughout the design process of the facility. The proposed dynamic...

59. The Geometry of Reinforcement Learning: Insights from the Dual Linear Program

Nikola Milosevic (Max Planck Institute for Human Cognitive and Brain Sciences)

Student Talk

Reinforcement Learning (RL) has become a cornerstone of machine learning, showcasing remarkable success in addressing real-world control problems and providing insights into cognitive processes in the brain. However, navigating the intricacies of modern RL proves challenging due to its numerous moving parts, escalating agent complexity, and the application of deep learning in a non-i.i.d....