6 papers
Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
Seyed Mahdi B. Azad, Jasper Hoffmann, Iman Nematollahi +3
Forward-backward (FB) representations provide a powerful framework for learning the successor representation (SR) in continuous spaces by enforcing a low-rank factorization. Howeve…
Fitting Reinforcement Learning Model to Behavioral Data under Bandits
Hao Zhu, Jasper Hoffmann, Baohe Zhang +1
We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attenti…
Differentiable Nonlinear Model Predictive Control
Jonathan Frey, Katrin Baumgärtner, Gianluca Frison +5
The efficient computation of parametric solution sensitivities is a key challenge in the integration of learning-enhanced methods with nonlinear model predictive control (MPC), as…
Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality
Akhil S Anand, Shambhuraj Sawant, Paavo Parmas +3
Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive. Th…
Multi-convex Programming for Discrete Latent Factor Models Prototyping
Hao Zhu, Shengchao Yan, Jasper Hoffmann +1
Discrete latent factor models (DLFMs) are widely used in various domains such as machine learning, economics, neuroscience, psychology, etc. Currently, fitting a DLFM to some datas…
Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification
Rudolf Reiter, Jasper Hoffmann, Dirk Reinhardt +6
The fields of MPC and RL consider two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are wid…