no code implementations • 25 Sep 2022 • Upadhi Vijay, Soomin Woo, Scott J. Moura, Akshat Jain, David Rodriguez, Sergio Gambacorta, Giuseppe Ferrara, Luigi Lanuzza, Christian Zulberti, Erika Mellekas, Carlo Papa
This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data.
no code implementations • 4 Jun 2021 • Mathilde D. Badoual, Scott J. Moura
To fill these gaps, we implement an online Supervised Actor-Critic (SAC) algorithm, supervised with a model-based controller -- Model Predictive Control (MPC).
no code implementations • 8 Apr 2021 • Sangjae Bae, David Isele, Kikuo Fujimura, Scott J. Moura
This paper proposes a discretionary lane selection algorithm.
1 code implementation • 2 Apr 2020 • Aaron Kandel, Scott J. Moura
In particular, many control-theoretic LbC methods require subject matter expertise in order to translate their own safety guarantees, often manifested as preexisting data of safe trajectories or structural model knowledge.
no code implementations • 7 Feb 2020 • Aaron Kandel, Scott J. Moura
This paper presents a distributionally robust Q-Learning algorithm (DrQ) which leverages Wasserstein ambiguity sets to provide idealistic probabilistic out-of-sample safety guarantees during online learning.
1 code implementation • 21 Jan 2020 • Laurel N. Dunn, Ioanna Kavvada, Mathilde Badoual, Scott J. Moura
This work formulates a Bayesian hierarchical framework designed to integrate data and domain expertise to understand the failure properties of a regional power system, where variability in the expected performance of individual components gives rise to failure processes that are heterogeneous and uncertain.
no code implementations • 4 Aug 2019 • Abhishek Halder, Kenneth F. Caluya, Bertrand Travacca, Scott J. Moura
We provide gradient flow interpretations for the continuous-time continuous-state Hopfield neural network (HNN).