94 lines
5.8 KiB
BibTeX
94 lines
5.8 KiB
BibTeX
@article{lu2022discovered,
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title={Discovered policy optimisation},
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author={Lu, Chris and Kuba, Jakub and Letcher, Alistair and Metz, Luke and Schroeder de Witt, Christian and Foerster, Jakob},
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journal={Advances in Neural Information Processing Systems},
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volume={35},
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pages={16455--16468},
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year={2022}
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}
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@software{jax2018github,
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author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
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title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
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url = {http://github.com/jax-ml/jax},
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version = {0.3.13},
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year = {2018},
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}
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@article{doi:10.1518/106480407X312374,
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author = {Antony Hilliard and Greg A. Jamieson},
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title ={Winning Solar Races with Interface Design},
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journal = {Ergonomics in Design},
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volume = {16},
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number = {2},
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pages = {6-11},
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year = {2008},
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doi = {10.1518/106480407X312374},
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URL = {https://doi.org/10.1518/106480407X312374},
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eprint = {https://doi.org/10.1518/106480407X312374},
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abstract = { Solar car racing is both a highly competitive sport and a test arena for tomorrow's renewable-energy applications. This article describes the design of a graphical interface for solar car race strategy planning. The coupling, unpredictability, and size of the solar car racing environment present tough challenges to racing strategy teams. Representation-aiding techniques provide a useful approach for managing this complexity, translating difficult problems into visual analogues that are better suited to human information processing. }
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}
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@Article{heuristicsolar,
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AUTHOR = {Betancur, Esteban and Osorio-Gómez, Gilberto and Rivera, Juan Carlos},
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TITLE = {Heuristic Optimization for the Energy Management and Race Strategy of a Solar Car},
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JOURNAL = {Sustainability},
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VOLUME = {9},
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YEAR = {2017},
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NUMBER = {10},
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ARTICLE-NUMBER = {1576},
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URL = {https://www.mdpi.com/2071-1050/9/10/1576},
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ISSN = {2071-1050},
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ABSTRACT = {Solar cars are known for their energy efficiency, and different races are designed to measure their performance under certain conditions. For these races, in addition to an efficient vehicle, a competition strategy is required to define the optimal speed, with the objective of finishing the race in the shortest possible time using the energy available. Two heuristic optimization methods are implemented to solve this problem, a convergence and performance comparison of both methods is presented. A computational model of the race is developed, including energy input, consumption and storage systems. Based on this model, the different optimization methods are tested on the optimization of the World Solar Challenge 2015 race strategy under two different environmental conditions. A suitable method for solar car racing strategy is developed with the vehicle specifications taken as an independent input to permit the simulation of different solar or electric vehicles.},
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DOI = {10.3390/su9101576}
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}
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@misc{gymnasium,
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title={Gymnasium: A Standard Interface for Reinforcement Learning Environments},
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author={Mark Towers and Ariel Kwiatkowski and Jordan Terry and John U. Balis and Gianluca De Cola and Tristan Deleu and Manuel Goulão and Andreas Kallinteris and Markus Krimmel and Arjun KG and Rodrigo Perez-Vicente and Andrea Pierré and Sander Schulhoff and Jun Jet Tai and Hannah Tan and Omar G. Younis},
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year={2024},
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eprint={2407.17032},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2407.17032},
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}
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@inproceedings{Ansel_PyTorch_2_Faster_2024,
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author = {Ansel, Jason and Yang, Edward and He, Horace and Gimelshein, Natalia and Jain, Animesh and Voznesensky, Michael and Bao, Bin and Bell, Peter and Berard, David and Burovski, Evgeni and Chauhan, Geeta and Chourdia, Anjali and Constable, Will and Desmaison, Alban and DeVito, Zachary and Ellison, Elias and Feng, Will and Gong, Jiong and Gschwind, Michael and Hirsh, Brian and Huang, Sherlock and Kalambarkar, Kshiteej and Kirsch, Laurent and Lazos, Michael and Lezcano, Mario and Liang, Yanbo and Liang, Jason and Lu, Yinghai and Luk, CK and Maher, Bert and Pan, Yunjie and Puhrsch, Christian and Reso, Matthias and Saroufim, Mark and Siraichi, Marcos Yukio and Suk, Helen and Suo, Michael and Tillet, Phil and Wang, Eikan and Wang, Xiaodong and Wen, William and Zhang, Shunting and Zhao, Xu and Zhou, Keren and Zou, Richard and Mathews, Ajit and Chanan, Gregory and Wu, Peng and Chintala, Soumith},
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booktitle = {29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24)},
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doi = {10.1145/3620665.3640366},
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month = apr,
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publisher = {ACM},
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title = {{PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation}},
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url = {https://pytorch.org/assets/pytorch2-2.pdf},
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year = {2024}
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}
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@article{stable-baselines3,
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author = {Antonin Raffin and Ashley Hill and Adam Gleave and Anssi Kanervisto and Maximilian Ernestus and Noah Dormann},
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title = {Stable-Baselines3: Reliable Reinforcement Learning Implementations},
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journal = {Journal of Machine Learning Research},
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year = {2021},
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volume = {22},
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number = {268},
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pages = {1-8},
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url = {http://jmlr.org/papers/v22/20-1364.html}
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}
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@software{gymnax2022github,
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author = {Robert Tjarko Lange},
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title = {{gymnax}: A {JAX}-based Reinforcement Learning Environment Library},
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url = {http://github.com/RobertTLange/gymnax},
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version = {0.0.4},
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year = {2022},
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}
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@misc{proximalpolicyoptimization,
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title={Proximal Policy Optimization Algorithms},
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author={John Schulman and Filip Wolski and Prafulla Dhariwal and Alec Radford and Oleg Klimov},
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year={2017},
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eprint={1707.06347},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/1707.06347},
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}
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