← Back to all news
arXiv AI By Riya Kinnarkar, Mansur M. Arief, Yan Pratama Akhra, Dino Arla

Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies

Read the original on arXiv AI →

arXiv:2607. 10201v1 Announce Type: cross Abstract: Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply between mature and emerging economies.

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 AI.

Related stories

arXiv AI
Jun 26

Power Couple? AI Growth and Renewable Energy Investment

arXiv:2603. 26678v2 Announce Type: replace-cross Abstract: AI and renewable energy are increasingly framed as a "power couple," on the premise that surging AI demand will accelerate clean-energy investment, yet concerns persist that AI will entrench fossil-fuel carbon lock-in.

By Luyi Gui, Tinglong Dai
More like this →
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
agentsreinforcement-learning
More like this →