Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

The Centre for Economic Policy Research (CEPR) and the European Central Bank (ECB) are hosting this joint conference The conference brings together academics and policy makers to discuss theoretical and practical aspects of central bank digital currencies (CBDCs). Relying on a combination of research presentations, keynote speeches and panel discussions, it aims to advance our understanding of the potential benefits and costs of CBDCs from a macroeconomic and policy perspective.

Photo: Bernd Roselieb for the ECB

Practical aspect of "to be or not to be?" - Traditional food of my hometown: krapfen, brioches, kipfel and some high fat percentage sweeties

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

The Centre for Economic Policy Research (CEPR) and the European Central Bank (ECB) are hosting this joint conference The conference brings together academics and policy makers to discuss theoretical and practical aspects of central bank digital currencies (CBDCs). Relying on a combination of research presentations, keynote speeches and panel discussions, it aims to advance our understanding of the potential benefits and costs of CBDCs from a macroeconomic and policy perspective.

Photo: Bernd Roselieb for the ECB

The Centre for Economic Policy Research (CEPR) and the European Central Bank (ECB) are hosting this joint conference The conference brings together academics and policy makers to discuss theoretical and practical aspects of central bank digital currencies (CBDCs). Relying on a combination of research presentations, keynote speeches and panel discussions, it aims to advance our understanding of the potential benefits and costs of CBDCs from a macroeconomic and policy perspective.

Photo: Bernd Roselieb for the ECB

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Within two days international expert on regional development processes, Ane Sindik (Croatia), acquainted the participants with the methodology and the practical aspects of development projects’ design.

Протягом двох днів міжнародний експерт з питань регіонального розвитку Ане Cиндік (Хорватія) знайомила учасників тренінгу з методологією та практичними аспектами розробки проектів розвитку.

Oberlin's Career Exploration and Development office celebrated the achievements of their Law & Justice Scholars in April while welcoming a new cohort into the program. The Law & Justice Scholars Program focuses on students understanding practical aspects of the law for those interested in careers focused on social justice policy and law.

Photo by Mike Crupi

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Oberlin's Career Exploration and Development office celebrated the achievements of their Law & Justice Scholars in April while welcoming a new cohort into the program. The Law & Justice Scholars Program focuses on students understanding practical aspects of the law for those interested in careers focused on social justice policy and law.

Photo by Mike Crupi

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

The Sustainable Offsite Construction project equips students with crucial project management skills while immersing them in modern digital construction technologies. It also underscores the critical importance of scheduling, teamwork, financial control, health and safety, and the practical aspects of engineering theory.

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

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Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

The Centre for Economic Policy Research (CEPR) and the European Central Bank (ECB) are hosting this joint conference The conference brings together academics and policy makers to discuss theoretical and practical aspects of central bank digital currencies (CBDCs). Relying on a combination of research presentations, keynote speeches and panel discussions, it aims to advance our understanding of the potential benefits and costs of CBDCs from a macroeconomic and policy perspective.

Photo: Bernd Roselieb for the ECB

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

A weekend trip to Disney to celebrate her birthday! This smile was the best part of the weekend! The older 50mm 1.4 was used for this, still trying to decide if I let my desire for newer looking lenses override the practical aspect that this lens just works for far less money.

April 23, 2018 through April 29, 2018

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser

Reinforcement learning (RL) is a powerful learning paradigm of machine learning (ML). It is particularly suited to tackle control problems in large environments, can learn from experience without the need of a model of the dynamics, and can deal with delayed consequences.

Capturing your control problem as a meaningful Markov Decision Process (MDP) is not trivial. Additional challenges arise in the training in terms of stability and evaluation. Other practical aspects include reproducibility, efficiency, implementation, deployment in hardware, or choosing the most suitable algorithm for your problem.

RL applications in particle accelerators are very promising, but have been deployed in real machines only a handful of times. This workshop aims at lowering the barrier in applying RL and making it a more widely used tool.

Photos: Simon P. Haigermoser