Novel Title: “Learning to Coordinate: A Submodular Multi-Agent Reinforcement Learning Framework for Spatial Coverage and Performance Bounds” Novel Abstract: This paper introduces a novel multi-agent reinforcement learning framework for spatial coverage optimization, where agents must learn coordinated placements to maximize event detection in complex environments. By reformulating the coverage problem as a submodular Markov decision process, we develop a greedy learning policy that exploits diminishing returns in collaborative sensing. We propose a new curvature-based learning bound that guarantees near-optimal performance regardless of agent sensing capabilities, improving upon classical approximation guarantees. Our approach integrates both joint and max-detection mechanisms through a tunable cooperation parameter, enabling flexible trade-offs between individualism and collaboration. To validate our framework, we implement an interactive learning simulator and conduct experiments across varied obstacle layouts and sensing configurations. Results show that our curvature-driven bounds consistently outperform standard RL guarantees, particularly in dense sensing regimes, while maintaining computational tractability. This work bridges submodular optimization and multi-agent learning, offering both theoretical assurances and practical tools for coverage tasks in surveillance, environmental monitoring, and distributed sensing networks.