1 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Solving Markov Decision Processes with Future Information via MPC
Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt +1
Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based p…
Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
Guanru Pan, Dirk Reinhardt, Sebastien Gros +1
This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances,…
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…
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control
Dirk Reinhardt, Katrin Baumgärnter, Jonathan Frey +2
In this paper, we present an early software integrating Reinforcement Learning (RL) with Model Predictive Control (MPC). Our aim is to make recent theoretical contributions from th…
Economic Model Predictive Control as a Solution to Markov Decision Processes
Dirk Reinhardt, Akhil S. Anand, Shambhuraj Sawant +1
Markov Decision Processes (MDPs) offer a fairly generic and powerful framework to discuss the notion of optimal policies for dynamic systems, in particular when the dynamics are st…
Data-Driven Domestic Flexible Demand: Observations from experiments in cold climate
Dirk Reinhardt, Wenqi Cai, Sebastien Gros
In this chapter, we report on our experience with domestic flexible electric energy demand based on a regular commercial (HVAC)-based heating system in a house. Our focus is on inv…