1 citations · 1 across the 2 of their papers we have counts for
9 papers
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…
Error whitening: Why Gauss-Newton outperforms Newton
Maricela Best McKay, Nathan P. Lawrence, Brian Wetton +1
The Gauss-Newton matrix is widely viewed as a positive semidefinite approximation of the Hessian, yet mounting empirical evidence shows that Gauss-Newton descent outperforms Newton…
The Separation Principle and the Dual-Certainty Equivalence Gap in Model Predictive Control
Tren Baltussen, Nathan P. Lawrence, Alexander Katriniok +2
Dual control addresses the trade-off between exploitation and exploration, where control inputs both regulate the system and generate informative data for estimation and identifica…
Soft MPCritic: Amortized Model Predictive Value Iteration
Thomas Banker, Nathan P. Lawrence, Ali Mesbah
Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet combining them at scale remains computationally challenging. We propose soft MPCri…
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…
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…