RangL: A Reinforcement Learning Competition Platform
Abstract
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.
Cite
@article{arxiv.2208.00003,
title = {RangL: A Reinforcement Learning Competition Platform},
author = {Viktor Zobernig and Richard A. Saldanha and Jinke He and Erica van der Sar and Jasper van Doorn and Jia-Chen Hua and Lachlan R. Mason and Aleksander Czechowski and Drago Indjic and Tomasz Kosmala and Alessandro Zocca and Sandjai Bhulai and Jorge Montalvo Arvizu and Claude Klöckl and John Moriarty},
journal= {arXiv preprint arXiv:2208.00003},
year = {2022}
}
Comments
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!