RangL: A Reinforcement Learning Competition Platform

Autor:	Zobernig, Viktor, Saldanha, Richard A., He, Jinke, van der Sar, Erica, van Doorn, Jasper, Hua, Jia-Chen, Mason, Lachlan R., Czechowski, Aleksander, Indjic, Drago, Kosmala, Tomasz, Zocca, Alessandro, Bhulai, Sandjai, Arvizu, Jorge Montalvo, Klöckl, Claude, Moriarty, John
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - General Literature Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems. This article describes the reusable code repository developed by the RangL team and deployed for the 2022 Pathways to Net Zero Challenge, supported by the UK Net Zero Technology Centre. The winning solutions to this particular Challenge seek to optimize the UK's energy transition policy to net zero carbon emissions by 2050. The RangL repository includes an OpenAI Gym reinforcement learning environment and code that supports both submission to, and evaluation in, a remote instance of the open source EvalAI platform as well as all winning learning agent strategies. The repository is an illustrative example of RangL's capability to provide a reusable structure for future challenges. Comment: Documents in general and premierly the RangL competition plattform and in particular its 2022's competition "Pathways to Netzero" 10 pages, 2 figures, 1 table, Comments welcome!
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.00003 Zobrazit plný text záznamu View this record from Arxiv