uav-marl-hotspot-benchmark

You can benchmark multi-agent reinforcement learning for UAV hotspot management by simulating multi-cluster user environments, comparing MAPPO and Beta distribution policies, and evaluating centralized versus decentralized controllers under dynamic mobility constraints.

The best way to assign UAVs to dispersed user clusters involves multi-cluster user grouping combined with Hungarian matching at episode start, optimizing UAV trajectories and resource distribution across hotspot regions.

Yes, you can compare centralized and decentralized controllers for UAV hotspot coordination by evaluating their performance and robustness under dynamic mobility and resource constraints using supported policy architectures like Gaussian and Beta distributions.

Yes, the multi-agent UAV framework supports Beta distribution policies alongside MAPPO strategies, facilitating comprehensive scenario comparisons for UAV trajectory training and resource allocation in dispersively hot-spot environments.

You simulate multi-region user scenarios for UAV deployment by running the hotspot MARL script with the environment configured to the dispersed clusters scenario, enabling multi-cluster user grouping and assignment.

You need numpy, scipy, matplotlib, pandas, and torch installed to run multi-agent reinforcement learning benchmarks for UAV coordination, supporting clustering, assignment, and control strategy evaluation.