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.

翻译：由Alan Turing研究所主办的RangL项目旨在通过支持与现实世界动态决策问题有关的竞争,鼓励更广泛地接受强化学习,这一条描述了RangL团队开发的可再使用代码库,该代码库是为2022年净零挑战路径部署的,得到了英国零净技术中心的支持。这一特殊挑战的得胜解决方案力求在2050年前优化英国能源过渡政策,实现净零碳排放。RangL存储库包括一个OpenAI Gym强化学习环境和代码,支持向开放源EvalAI平台的远程实例提交和评价,以及所有成功学习代理战略。该存储库是RangL有能力为未来挑战提供可再使用结构的一个示例。