1 citations · 1 across the 7 of their papers we have counts for
9 papers
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…
CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg +2
Combinatorial sequential decision making problems are typically modeled as mixed integer linear programs (MILPs) and solved via branch and bound (B&B) algorithms. The inherent diff…
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…
Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations
Vyacheslav Kungurtsev, Alessandro Di Frenna, Gustav Sir +5
Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots…
All AI Models are Wrong, but Some are Optimal
Akhil S Anand, Shambhuraj Sawant, Dirk Reinhardt +1
AI models that predict the future behavior of a system (a.k.a. predictive AI models) are central to intelligent decision-making. However, decision-making using predictive AI models…
Optimality Conditions for Model Predictive Control: Rethinking Predictive Model Design
Akhil S Anand, Arash Bahari Kordabad, Mario Zanon +1
Optimality is a critical aspect of Model Predictive Control (MPC), especially in economic MPC. However, achieving optimality in MPC presents significant challenges, and may even be…