From c5515da83cdd655bd71e2d24acec1a237cf67d42 Mon Sep 17 00:00:00 2001 From: zxymark221 Date: Wed, 19 Oct 2022 20:51:37 -0600 Subject: [PATCH] Update citation info. --- README.md | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 082bc02..7b3017c 100644 --- a/README.md +++ b/README.md @@ -25,8 +25,8 @@ cases, at least some subset of agents incorporates elements of the power flow solution at each time step as part of their reward (negative cost) structures. -Please refer to our [preprint on arXiv](https://arxiv.org/abs/2111.05969) for -more details. Data and run scripts used to generate figures in the preprint +Please refer to our [published paper](https://dl.acm.org/doi/abs/10.1145/3538637.3539616) or [preprint on arXiv](https://arxiv.org/abs/2111.05969) for +more details. Data and run scripts used to generate figures in the paper are available in the [`paper`](./paper) directory. ### Basic installation instructions @@ -67,11 +67,22 @@ the Laboratory Directed Research and Development (LDRD) Program at NREL. If citing this work, please use the following: ```bibtex -@article{biagioni2021powergridworld, - title={PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems}, - author={Biagioni, David and Zhang, Xiangyu and Wald, Dylan and Vaidhynathan, Deepthi and Chintala, Rohit and King, Jennifer and Zamzam, Ahmed S}, - journal={arXiv preprint arXiv:2111.05969}, - year={2021} + +@inproceedings{biagioni2021powergridworld, + author = {Biagioni, David and Zhang, Xiangyu and Wald, Dylan and Vaidhynathan, Deepthi and Chintala, Rohit and King, Jennifer and Zamzam, Ahmed S.}, + title = {PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems}, + year = {2022}, + isbn = {9781450393973}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + url = {https://doi.org/10.1145/3538637.3539616}, + doi = {10.1145/3538637.3539616}, + booktitle = {Proceedings of the Thirteenth ACM International Conference on Future Energy Systems}, + pages = {565–570}, + numpages = {6}, + keywords = {deep learning, power systems, OpenAI gym, reinforcement learning, multi-agent systems}, + location = {Virtual Event}, + series = {e-Energy '22} } ```