用强化学习求解碳信用市场纳什均衡，帮企业省下可观减排成本。
Multi-Agent Reinforcement Learning for Greenhouse Gas Offset Credit Markets
- 用Nash-DQN算法估算碳信用市场的纳什均衡
- 数值实验显示遵守均衡可让排放企业节省显著成本
- 适合关注气候金融与智能博弈的从业者
气候变化是人类未来的重大威胁，主要由人为温室气体排放加剧。政府可通过设定排放上限并惩罚超限排放来控制。企业也可通过投资减排或捕碳项目生成碳信用额度，用于抵消自身超排，或在市场中交易。本文刻画了有限参与者碳信用市场的纳什均衡，并采用现代强化学习方法Nash-DQN高效估算该均衡。实验验证了强化学习在气候金融市场的有效性，且结果显示，遵循纳什均衡的企业可在数值模拟中实现显著财务节约。
原文摘要 · Abstract (English)
Climate change is a major threat to the future of humanity, and its impacts are being intensified by excess man-made greenhouse gas emissions. One method governments can employ to control these emissions is to provide firms with emission limits and penalize any excess emissions above the limit. Excess emissions may also be offset by firms who choose to invest in carbon reducing and capturing projects. These projects generate offset credits which can be submitted to a regulating agency to offset a firm's excess emissions, or they can be traded with other firms. In this work, we characterize the finite-agent Nash equilibrium for offset credit markets. As computing Nash equilibria is an NP-hard problem, we utilize the modern reinforcement learning technique Nash-DQN to efficiently estimate the market's Nash equilibria. We demonstrate not only the validity of employing reinforcement learning methods applied to climate themed financial markets, but also the significant financial savings emitting firms may achieve when abiding by the Nash equilibria through numerical experiments.
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