1 citations · 2 across the 5 of their papers we have counts for
5 papers
Soft MPCritic: Value-Based Reinforcement Learning for Non-Differentiable Predictive Control
Thomas Banker, Nathan P. Lawrence, Ali Mesbah
Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet existing MPC-RL frameworks commonly rely on gradient-based optimization and theref…
Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents
Thomas Banker, Ali Mesbah
Training sophisticated agents for optimal decision-making under uncertainty has been key to the rapid development of modern autonomous systems across fields. Notably, model-free re…
MPCritic: A plug-and-play MPC architecture for reinforcement learning
Nathan P. Lawrence, Thomas Banker, Ali Mesbah
The reinforcement learning (RL) and model predictive control (MPC) communities have developed vast ecosystems of theoretical approaches and computational tools for solving optimal…
Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learning
Thomas Banker, Nathan P. Lawrence, Ali Mesbah
Making optimal decisions under uncertainty is a shared problem among distinct fields. While optimal control is commonly studied in the framework of dynamic programming, it is appro…
Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces
Farshud Sorourifar, Thomas Banker, Joel A. Paulson
Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive s…