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Data Efficient Reinforcement Learning
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Room: Drago-Adeje
Reinforcement Learning (RL) has seen some spectacular developments recently. One of the key challenges for many applications facing RL is data efficiency, i.e., how to learn from limited data. In this workshop we want to focus on aspects from various disciplines including control, robotics, personalized healthcare and machine learning, which are central to progress on data-efficient RL, such as probabilistic methods, approximation techniques, experimental design, the exploration/exploitation tradeoff, data efficiency benchmarking and others.Speakers:
- Aldo Faisal
- Max Jaderberg
- Melanie Zeilinger
- Roberto Calandra
- Thomas Schön
- Chris Watkins
- Pierre-Luc Bacon
09:30-10:00 Learning flexible models of nonlinear dynamical systems
10:00-10:30 Towards Safe Learning during Closed-loop Control
10:30-11:00 Goal-Driven Dynamics Learning for Model-Based RL
11:30-12:00 When the patient in front of you is the data source: (Machine) learning to adapt in real-time to acute clinical settings
12:00-12:30 Unifying Multi-Step Reinforcement Learning Methods through Matrix Splittings
12:30-13:00 Unsupervised Learning for RL
18:00-18:30 Innate Knowledge
18:30-20:00 Panel discussion