RangL: A Reinforcement Learning Competition Platform

TL;DR

RangL is a platform that facilitates reinforcement learning competitions focused on real-world problems, exemplified by a challenge to optimize the UK's energy policy for net zero emissions by 2050.

Contribution

It introduces a reusable code repository and environment for RL competitions, supporting real-world decision problems and enabling evaluation on a remote platform.

Findings

Successful deployment for the 2022 Net Zero Challenge

Winning solutions optimize UK's energy transition policies

Repository supports multiple RL agents and evaluation methods

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.