25 citations · 29 across the 9 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…
Direct transfer of optimized controllers to similar systems using dimensionless MPC
Josip Kir Hromatko, Shambhuraj Sawant, Šandor Ileš +1
Scaled model experiments are commonly used in various engineering fields to reduce experimentation costs and overcome constraints associated with full-scale systems. The relevance…
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…
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…
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 Predictive Control and MPC: Do we achieve optimality?
Akhil S Anand, Shambhuraj Sawant, Dirk Reinhardt +1
In this paper, we explore the interplay between Predictive Control and closed-loop optimality, spanning from Model Predictive Control to Data-Driven Predictive Control. Predictive…
Learning-based MPC from Big Data Using Reinforcement Learning
Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt +1
This paper presents an approach for learning Model Predictive Control (MPC) schemes directly from data using Reinforcement Learning (RL) methods. The state-of-the-art learning meth…
Bridging the gap between QP-based and MPC-based RL
Shambhuraj Sawant, Sebastien Gros
Reinforcement learning methods typically use Deep Neural Networks to approximate the value functions and policies underlying a Markov Decision Process. Unfortunately, DNN-based RL…
Sample-efficient Cross-Entropy Method for Real-time Planning
Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes +4
Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse…