论文标题
Rangl：增强学习竞赛平台
RangL: A Reinforcement Learning Competition Platform
论文作者
论文摘要
艾伦·图灵研究所（Alan Turing Institute）主持的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来鼓励更广泛的强化学习。本文介绍了由Rangl团队开发的可重复使用的代码存储库，并在英国净零技术中心支持的2022年净零挑战赛的2022途径中进行了部署。到2050年，针对这一特殊挑战的获胜解决方案旨在优化英国的能源过渡政策，以便到2050年净碳排放量。RANGL存储库包括一个OpenAI Gym增强的学习环境和代码，该环境和代码都支持提交和评估开源的远程实例开放源代码eartai Platform以及所有获奖的学习代理策略。存储库是Rangl能力为未来挑战提供可重复使用的结构的一个说明性示例。
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.
