RangL: A Reinforcement Learning Competition Platform
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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.
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References · 5
- 2019A Survey of Deep Reinforcement Learning in Video Gamescited by 73
- 2021Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policiescited by 37
- 2019Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environmentscited by 13
- 2021Computational Performance of Deep Reinforcement Learning to find Nash Equilibriacited by 3
- 2021On Incorporating Forecasts into Linear State Space Model Markov Decision Processescited by 2
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