Strategic Investment Decision Making for Value Creation in Energy Transition: A Reinforcement Learning Approach

Key points

• The global challenge of climate change has driven significant steps to reduce CO2 emissions, guided by international agreements like the Paris Agreement of 2015.1
• Building on this, we designed a multi-criteria SDM framework that explores various decision strategies related to different portfolios for allocating funds across three sectors: oil & gas, renewables, and CO2 reduction.1
• This research evaluates the use of Reinforcement Learning (RL) to identify optimal investment policies within the defined SDM framework.1
• The agent's sequential decisions shape a virtual dynamic environment by influencing key variables such as oil and gas production, renewable energy output, CO2 emissions, and revenues.1

Sources (1)

• [1]Strategic Investment Decision Making for Value Creation in Energy Transition: A Reinforcement Learning Approach
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 06:29 PM

  To navigate this complex transition, energy companies must adopt Sequential Decision Making (SDM) strategies to maximize value creation from decision flexibility under uncertainties.

  The global challenge of climate change has driven significant steps to reduce CO2 emissions, guided by international agreements like the Paris Agreement of 2015.

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