# Autonomous AI Agents for Real-Time Affordable Housing Site Selection: Multi-Objective Reinforcement Learning Under Regulatory Constraints

arXiv: https://arxiv.org/abs/2602.03940 · HTML: https://mlsanity.com/paper/2602.03940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)

## Abstract (arXiv)

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.