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Found 7 papers, 2 papers with code
Valuation of Public Bus Electrification with Open Data
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.
A Learning-based Optimal Market Bidding Strategy for Price-Maker Energy Storage
To fill these gaps, we implement an online Supervised Actor-Critic (SAC) algorithm, supervised with a model-based controller -- Model Predictive Control (MPC).
Risk-Aware Lane Selection on Highway with Dynamic Obstacles
This paper proposes a discretionary lane selection algorithm.
Safe Learning MPC with Limited Model Knowledge and Data
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.
Safe Wasserstein Constrained Deep Q-Learning
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.
Bayesian Hierarchical Methods for Modeling Electrical Grid Component Failures
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.
Hopfield Neural Network Flow: A Geometric Viewpoint
We provide gradient flow interpretations for the continuous-time continuous-state Hopfield neural network (HNN).
