no code implementations • 17 Jul 2023 • Patrick Emami, Xiangyu Zhang, David Biagioni, Ahmed S. Zamzam
In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy.
1 code implementation • 20 Dec 2022 • Patrick Emami, Aidan Perreault, Jeffrey Law, David Biagioni, Peter C. St. John
We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence.
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.
1 code implementation • 10 Nov 2021 • David Biagioni, Xiangyu Zhang, Dylan Wald, Deepthi Vaidhynathan, Rohit Chintala, Jennifer King, Ahmed S. Zamzam
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL).
Multi-agent Reinforcement Learning reinforcement-learning +1
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 • 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.