arXiv Machine Learning October 9, 2026 By Yasaman Cheraghi (Department of Energy and Petroleum Engineering, University of Stavanger, Norway), Reidar B. Bratvold (Department of Energy and Petroleum Engineering, University of Stavanger, Norway), Aojie Hong (Independent Researcher, Stavanger, Norway), Ressi B. Muhammad (Department of Energy and Petroleum Engineering, University of Stavanger, Norway), Sergey Alyaev (NORCE Norwegian Research Centre, Bergen, Norway)

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

Read the original on arXiv Machine Learning →

The paper presents a reinforcement learning framework for strategic investment decisions in the energy transition, aiming to balance profit, CO2 social costs, and competitive advantage across oil & gas, renewables, and CO2 reduction sectors. A custom simulation environment models the dynamic energy landscape up to 2050, allowing the RL agent to learn optimal policies that adapt to uncertainties in production, prices, and costs. Benchmarking shows the RL strategy consistently outperforms manually defined baseline policies in adaptability and long-term value creation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 2

Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings

arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.

By Hallah Shahid Butt, Qiong Huang, G\"okhan Demirel, Kevin F\"orderer, Erfan Tajalli-Ardekani, Simnon Waczowicz, Luigi Spatafora, Veit Hagenmeyer, Benjamin Sch\"afer

agents reinforcement-learning

arXiv Machine Learning

Sep 22

Accounting for Optimal Control in the Sizing of Isolated Hybrid Renewable Energy Systems Using Imitation Learning

arXiv:2601.03679v2 Announce Type: replace-cross Abstract: Decarbonization of isolated or off-grid energy systems through phase-in of large shares of intermittent solar or wind generation requires co-...

By Simon Halvdansson, Lucas Ferreira Bernardino, Brage Rugstad Knudsen

arXiv AI

Sep 7

Reinforcement Learning for Sequential Solar PV Policy Design under Uncertainty: An Agent-Based Approach

The paper presents a reinforcement learning framework for designing solar PV adoption policies under uncertainty, integrating RL with a stochastic agent‑based model to simulate yearly adoption over a 16‑year horizon. Policymakers can choose annual incentives such as grants, subsidised loans, and feed‑in tariffs, and the study evaluates three RL algorithms—PPO, SAC, and TD3—within a scalarised reward framework that balances adoption gains against costs. Results show clear trade‑off patterns, with TD3 yielding the highest adoption at higher cost, PPO achieving the lowest cost with fewer adopters, and a balanced PPO policy offering a middle ground, all outperforming static baseline policies.

By Iias Faiud, Jonaid Shianifar, Michael Schukat, Karl Mason

agents reinforcement-learning

arXiv Machine Learning

Sep 14

Towards Sustainable Hydrogen Systems: Supply Chain Optimization with Model Predictive Control and Reinforcement Learning

This study evaluates four control strategies—rule-based, model predictive control (MPC), reinforcement learning without forecasts (RL‑NF), and reinforcement learning with forecasts (RL‑F)—for a renewable‑powered hydrogen supply chain. Using a unified, physically realistic simulation that includes electrolyzer constraints, storage dynamics, and grid limits, the authors find that MPC delivers the best economic performance by leveraging short‑term forecasts, while RL‑NF performs robustly without future information. RL‑F does not consistently outperform RL‑NF, indicating that forecast uncertainty and added state complexity can hinder forecast‑augmented learning.

By Mahammad Valiyev

reinforcement-learning

arXiv Machine Learning

Jun 25

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.

By Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 11

Control-Oriented Scenario Tree Construction through Reinforcement Learning

arXiv:2608. 09335v1 Announce Type: new Abstract: Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sampled forecasts.

By Fabio Pavirani, Bert Claessens, Pierre Pinson, Chris Develder

reinforcement-learning

One email a morning, machine-written

Related stories

Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings

Accounting for Optimal Control in the Sizing of Isolated Hybrid Renewable Energy Systems Using Imitation Learning

Reinforcement Learning for Sequential Solar PV Policy Design under Uncertainty: An Agent-Based Approach

Towards Sustainable Hydrogen Systems: Supply Chain Optimization with Model Predictive Control and Reinforcement Learning

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

Control-Oriented Scenario Tree Construction through Reinforcement Learning