Scheduled presentations:

• Conditional Generative Models for Robot Control: new insights and perspectives
  *F. Califano (University of Twente), N. Botteghi (University of Twente)
• Co-design Optimization of Adaptive Structures using Deep Reinforcement Learning
  *A. Buda (Imperial College London), D. Grover (Technical University of Munich), S. Armanini (Technical University of Munich), U. Fasel (Imperial College London)
• Real-Time Optimal Control of Parametrized Systems by Deep Learning-Based Reduced Order Models
  *M. Tomasetto (Politecnico di Milano), F. Braghin (Politecnico di Milano), A. Manzoni (Politecnico di Milano)
• HydroGym: A Modern Reinforcement Learning Platform for Flow Control
  *L. Paehler (Technical University of Munich), J. Callaham (University of Washington), S. Ahnert (University of Washington), N. Adams (Technical University of Munich), S. Brunton (University of Washington)
• Approximated Bayesian Reinforcement Learning
  *V. Arnaoutis (University of Twente), B. Rosic (University of Twente)
• Matryoshka policy gradient for control problems
  *M. Han Veiga (Ohio State University), F. Ged (Swiss Federal Institute of Technology Lausann)