The energy transition potentially poses an existential risk for major
international oil companies (IOCs) if they fail to adapt to low-carbon business
models. Projections of energy futures, however, are met with diverging
assumptions on its scale and pace, causing disagreement among IOC
decision-makers and their stakeholders over what the business model of an
incumbent fossil fuel company should be. In this work, we used deep multi-agent
reinforcement learning to solve an energy systems wargame wherein players
simulate IOC decision-making, including hydrocarbon and low-carbon investments
decisions, dividend policies, and capital structure measures, through an
uncertain energy transition to explore critical and non-linear governance
questions, from leveraged transitions to reserve replacements. Adversarial play
facilitated by state-of-the-art algorithms revealed decision-making strategies
robust to energy transition uncertainty and against multiple IOCs. In all
games, robust strategies emerged in the form of low-carbon business models as a
result of early transition-oriented movement. IOCs adopting such strategies
outperformed business-as-usual and delayed transition strategies regardless of
hydrocarbon demand projections. In addition to maximizing value, these
strategies benefit greater society by contributing substantial amounts of
capital necessary to accelerate the global low-carbon energy transition. Our
findings point towards the need for lenders and investors to effectively
mobilize transition-oriented finance and engage with IOCs to ensure responsible
reallocation of capital towards low-carbon business models that would enable
the emergence of fossil fuel incumbents as future low-carbon leaders.

本文采用深度多智能体强化学习解决了一个能源系统的战争游戏，研究了国际石油公司在能源转型中应如何制定决策，并发现通过形成低碳业务模式可以实现最大价值并为全球低碳能源转型作出贡献。

深度多智能体强化学习揭示稳健的石油和天然气公司宏观策略

Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning

Oil companies are among the largest companies in the world whose economic
indicators in the global stock market have a great impact on the world economy
and market due to their relation to gold, crude oil, and the dollar. To
quantify these relationships, we use correlation features and relationships
between stocks with the dollar, crude oil, gold, and stock indices of major oil
companies, create data sets, and perform continuous and discrete correlation
analyses with each other. To predict the stocks of different companies, we use
Recurrent Neural Networks (RNNs) and LSTM, because these stocks change in time
series. We carry out empirical experiments and perform on the stock indices
dataset to evaluate the prediction performance in terms of several common error
metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean
Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The received
results are promising and present a reasonably accurate prediction for the
price of oil companies' stocks in the near future. Despite the volatility of
the investigated systems in continuous and discrete correlation analysis, LSTM
has a high interpretability ability to investigate surprising.

该研究使用相关分析和神经网络算法，研究了石油公司股票与黄金、原油、美元等因素的关系，成功预测了不同公司股票未来价格趋势，结果具有良好的可解释性。