no code implementations • 7 Feb 2024 • Peter Graf, Patrick Emami
Models and policies that are simultaneously differentiable and interpretable may be key enablers of this marriage.
1 code implementation • NeurIPS 2023 • Patrick Emami, Abhijeet Sahu, Peter Graf
We also show that fine-tuning pretrained models on real commercial and residential buildings improves performance for a majority of target buildings.
1 code implementation • 18 Oct 2022 • David Biagioni, Xiangyu Zhang, Christiane Adcock, Michael Sinner, Peter Graf, Jennifer King
We demonstrate, in this context, that hybrid methods offer many benefits over both purely model-free and model-based methods as long as certain requirements are met.
no code implementations • 8 Nov 2021 • David J. Biagioni, Xiangyu Zhang, Peter Graf, Devon Sigler, Wesley Jones
We demonstrate that optimal control for this problem is challenging, requiring more than 8-hour lookahead for MPC with perfect forecasting to attain the minimum cost.
no code implementations • 5 Oct 2021 • Yi Hou, Peter Graf
The emergence of deep reinforcement learning (RL) and connected and automated vehicle technology provides a possible solution to improve mobility and energy efficiency at freeway bottlenecks through cooperative lane changing.
no code implementations • 27 May 2021 • Erotokritos Skordilis, Yi Hou, Charles Tripp, Matthew Moniot, Peter Graf, David Biagioni
To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost.
no code implementations • 13 Apr 2020 • Eric B. Jones, Peter Graf, Eliot Kapit, Wesley Jones
The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown.
no code implementations • 8 Nov 2019 • David Biagioni, Peter Graf, Xiangyu Zhang, Ahmed Zamzam, Kyri Baker, Jennifer King
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions.
1 code implementation • 27 Sep 2019 • Félix Therrien, Peter Graf, Vladan Stevanović
Finding an optimal match between two crystal structures underpins many important materials science problems including describing solid-solid phase transitions, developing models for interface and grain boundary structures, etc.
Materials Science Computational Physics