5 papers · 1 filter
Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning
Amber Srivastava
Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, however, the environment itself is alter…
Sparse Linear Regression with Constraints: A Flexible Entropy-based Framework
Amber Srivastava, Alisina Bayati, Srinivasa Salapaka
This work presents a new approach to solve the sparse linear regression problem, i.e., to determine a k-sparse vector w in R^d that minimizes the cost ||y - Aw||^2_2. In contrast t…
Once upon a time step: A closed-loop approach to robust MPC design
Anilkumar Parsi, Marcell Bartos, Amber Srivastava +2
A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility…
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint
Alisina Bayati, Amber Srivastava, Amir Malvandi +2
The industrial drying process consumes approximately 12% of the total energy used in manufacturing, with the potential for a 40% reduction in energy usage through improved process…
On Determining and Qualifying the Number of Superstates in Aggregation of Markov Chains
Amber Srivastava, Raj K. Velicheti, Srinivasa M. Salapaka
Many studies involving large Markov chains require determining a smaller representative (aggregated) chains. Each {\em superstate} in the representative chain represents a {\em gro…