490 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of British ColumbiaCA9 papers
- Herzberg Institute of AstrophysicsCA5 papers
- Space Telescope Science InstituteUS5 papers
- Université de MontréalCA5 papers
- Cornell UniversityUS4 papers
- University of MichiganUS4 papers
- American Museum of Natural HistoryUS3 papers
- Canadian Institute for Theoretical AstrophysicsCA3 papers
- Canadian Space AgencyCA3 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Goddard Space Flight CenterUS3 papers
- Johns Hopkins UniversityUS3 papers
5 papers · 1 filter
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…
Reinforcement Learning with Partial Parametric Model Knowledge
Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence +2
We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowl…
A modular framework for stabilizing deep reinforcement learning control
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang +2
We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guar…
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom +3
Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, ma…
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen +3
Deep reinforcement learning (RL) is an optimization-driven framework for producing control strategies for general dynamical systems without explicit reliance on process models. Goo…