# RangL: A Reinforcement Learning Competition Platform

arXiv: https://arxiv.org/abs/2208.00003 · HTML: https://mlsanity.com/paper/2208.00003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [A Survey of Deep Reinforcement Learning in Video Games](https://mlsanity.com/paper/1912.10944/) (2019)
- [Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies](https://mlsanity.com/paper/2111.02552/) (2021)
- [Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments](https://mlsanity.com/paper/1903.00827/) (2019)
- [Computational Performance of Deep Reinforcement Learning to find Nash Equilibria](https://mlsanity.com/paper/2104.12895/) (2021)
- [On Incorporating Forecasts into Linear State Space Model Markov Decision Processes](https://mlsanity.com/paper/2103.07533/) (2021)

## Abstract (arXiv)

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.