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I think it is a little too busy. Need to get some higher contrast between the dark and light fabrics.
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
Dismantling Greatness
TEDxDartmouth occurred on April 21st, 2018 and featured 12 amazing speakers in the fields of global health, athletics, computer science, psychology, and more.
Photo Credits: Arvind Suresh '19