Revealing the Oil Majors' Adaptive Capacity to the Energy Transition with Deep Multi-Agent Reinforcement Learning (Papers Track)

Dylan Radovic (Imperial College London); Lucas Kruitwagen (University of Oxford); Christian Schroeder de Witt (University of Oxford)

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Heavy Industry and Manufacturing Climate Finance & Economics Reinforcement Learning Uncertainty Quantification & Robustness


A low-carbon energy transition is transpiring to combat climate change, posing an existential threat to oil and gas companies, particularly the Majors. Though Majors yield the resources and expertise to adapt to low-carbon business models, meaningful climate-aligned strategies have yet to be enacted. A 2-degrees pathways (2DP) wargame was developed to assess climate-compatible pathways for the oil Majors. Recent advances in deep multi-agent reinforcement learning (MARL) have achieved superhuman-level performance in solving high-dimensional continuous control problems. Modeling within a Markovian framework, we present the novel 2DP-MARL model which applies deep MARL methods to solve the 2DP wargame across a multitude of transition scenarios. Designed to best mimic Majors in real- life competition, the model reveals all Majors quickly adapt to low-carbon business models to remain robust amidst energy transition uncertainty. The purpose of this work is provide tangible metrics to support the call for oil Majors to diversify into low-carbon business models and, thus, accelerate the energy transition.

Recorded Talk (direct link)