Over the past two decades, advances in computing and communications have resulted in the creation, transmission and storage of data from all sectors of society. Over the next decade, the biggest generator of data is expected to be Internet-of-Things devices which sense and control the physical world. This explosion of data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. The availability and scale of data, both temporal and spatial, brings a wonderful opportunity for our community to both advance the theory of control systems in a more data-driven fashion, as well as have a broader industrial and societal impact.
There are various challenges on the interface between the control community and the machine learning community. The goal of our workshop is to focus on what new ideas, approaches or questions can arise when learning theory is applied to control problems.In particular, our workshop goals are:
We are delighted to have assembled a world-class team of leading researchers working on the interface between machine learning and control.
For accomodation information please visit the conference page.
Registration for the workshop can be made through this link at the 57th IEEE Conference on Decision and Control website. Please note that only people who have registered for the conference can register for the workshop. The conference early registration rate is till October 1st. The workshop fees are as follows:
|Life Member||85 USD|
|8:30 - 8:45||Opening remarks by organizers|
|8:45 - 10:30||
Session 1: Reinforcement Learning for Control - Chair: Pramod P. Khargonekar
8:45 - 9:20 Aggregation, Rollout, Model Predictive Control, and Enhanced Policy Iteration in Reinforcement Learning - Dimitri P. Bertsekas (Massachusetts Institute of Technology)
9:20 - 9:55 Reinforcement Learning Structures for Real-Time Optimal Control and Differential Games - Frank L. Lewis (University of Texas at Arlington)
9:55 - 10:30 The Merits of Models in Continuous Reinforcement Learning - Benjamin Recht (University of California, Berkeley)
|10:30 - 11:00|| Break
|11:00 - 12:00||
Keynote Session - Chair: Manfred MorariDynamical, Symplectic and Stochastic Perspectives on Gradient-Based Optimization - Michael I. Jordan (University of California, Berkeley)
|12:00 - 1:30|| Lunch Break
|1:30 - 3:15||
Session 2: Optimization and Statistical Learning - Chair: George J. Pappas
2:40 - 3:15 Scenario Optimization for Robust Design - foundations and recent developments - Giuseppe Carlo Calafiore (Politecnico di Torino)
|3:15 - 3:30|| Break
|3:30 - 5:50||
Session 3: Safe Learning for Control - Chair: Konstantinos Gatsis
|5:50 - 6:00||Closing remarks by organizers|
Should you have any questions, please do not hesitate to contact the organizers.