3 citations · 5 across the 7 of their papers we have counts for
7 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…
Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control
Nathan P. Lawrence, Ali Mesbah
Goal-conditioned reinforcement learning (RL) concerns the problem of training an agent to maximize the probability of reaching target goal states. This paper presents an analysis o…
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…
User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization
Joshua Hang Sai Ip, Ankush Chakrabarty, Ali Mesbah +1
Incorporating user preferences into multi-objective Bayesian optimization (MOBO) allows for personalization of the optimization procedure. Preferences are often abstracted in the f…
A view on learning robust goal-conditioned value functions: Interplay between RL and MPC
Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes +2
Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty. Given the impact both fields h…