用强化学习快速选出符合法规的经济适用房选址，效率提升超90%。
Autonomous AI Agents for Real-Time Affordable Housing Site Selection: Multi-Objective Reinforcement Learning Under Regulatory Constraints
- 分层多智能体强化学习，兼顾法规约束与多重目标优化
- 合规率达94.3%，选址效率从18个月缩至72小时
- 适合城市规划、政策制定者及智慧城市建设团队
经济适用房短缺影响数十亿人，而土地稀缺与监管限制导致选址过程缓慢。我们提出AURA（Autonomous Urban Resource Allocator），一个在严格监管约束（QCT、DDA、LIHTC）下进行实时经济适用房选址的分层多智能体强化学习系统。将任务建模为受限多目标马尔可夫决策过程，优化可达性、环境影响、建设成本与社会公平性，并确保可行性。AURA采用包含127项联邦与地方约束的监管感知状态编码，结合具有可行性保障的帕累托约束策略梯度，以及将短期成本与长期社会效应分离的奖励分解机制。在8个美国大都会区的数据集（共47,392个候选地块）上，AURA实现94.3%的合规率，帕累托超体积优于强基线37.2%。在纽约市2026年案例研究中，选址时间从18个月缩短至72小时，识别出23%更多可行地块；所选地块交通可达性提升31%，环境影响降低19%，优于专家选择结果。
原文摘要 · Abstract (English)
Affordable housing shortages affect billions, while land scarcity and regulations make site selection slow. We present AURA (Autonomous Urban Resource Allocator), a hierarchical multi-agent reinforcement learning system for real-time affordable housing site selection under hard regulatory constraints (QCT, DDA, LIHTC). We model the task as a constrained multi-objective Markov decision process optimizing accessibility, environmental impact, construction cost, and social equity while enforcing feasibility. AURA uses a regulatory-aware state encoding 127 federal and local constraints, Pareto-constrained policy gradients with feasibility guarantees, and reward decomposition separating immediate costs from long-term social outcomes. On datasets from 8 U.S. metros (47,392 candidate parcels), AURA attains 94.3% regulatory compliance and improves Pareto hypervolume by 37.2% over strong baselines. In a New York City 2026 case study, it reduces selection time from 18 months to 72 hours and identifies 23% more viable sites; chosen sites have 31% better transit access and 19% lower environmental impact than expert picks.
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