CityPlanner: A New Sandbox Agent for Urban Planning
Urban planners face complex challenges in selecting feasible actions from large candidate spaces, often constrained by cost and service quality. Existing methods are effective but depend on task-specific representations and constraint handling. Researchers have introduced CityPlanner, a new sandbox-agent framework designed to address these issues. The framework includes UrbanSandbox, an…
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Key points
- CityPlanner introduces UrbanSandbox for executable urban planning.
- Atomic-task reinforcement learning decomposes long trajectories into BuildPlan and ImprovePlan.
- Experiments demonstrate CityPlanner's superior performance over existing methods.
Read the original at arXiv cs.AI · by Wentao Zhang, Jingyuan Wang, Zetong Zhou, Yifan Yang, Wenrui Wang primary sourceOpen source ↗
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