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

强化学习模型在不确定性下进行太阳能光伏政策设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了一种新颖的方法，将强化学习（RL）与随机基于代理的模型（ABM）相结合，用于设计太阳能光伏（PV）采用的序列式政策。该方法模拟了16年内的年采用情况，允许政策制定者代理选择年度激励措施，如资本补助和补贴贷款利率。研究表明，在采用收益和公共支出之间存在明显的权衡，不同的RL算法如PPO、SAC和TD3产生了稳健的模式。 AI

影响展示了RL在复杂、不确定环境中进行自适应政策设计的潜力。

排序理由该集群包含一篇详细介绍强化学习在新领域中新颖应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Iias Faiud, Jonaid Shianifar, Michael Schukat, Karl Mason · 2026-09-07 04:00

不确定性下太阳能光伏政策序列设计的强化学习：基于智能体的视角

arXiv:2609.04880v1 Announce Type: new Abstract: Designing effective and fiscally sustainable policies for solar photovoltaic (PV) adoption requires balancing adoption gains against public expenditure under uncertainty and heterogeneous decision-making. This study formulates PV po…

本文如何被排名

报道来源 [1]

不确定性下太阳能光伏政策序列设计的强化学习：基于智能体的视角

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