Byron Crowe

Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts

A prediction-aware adaptive rollout framework for heterogeneous multi-robot task assignment with scheduled and real-time requests that achieves near-complete service and reduces serviced-request wait times relative to reactive, token-passing, prediction-positioning, and myopic greedy baselines.