Lillian Ratliff and I received a gift from the UW+Amazon Science Hub to do research on the topic “Hierarchical framework for scalable multi-agent autonomous mobility”.

This proposal seeks to address the problem of large-scale multi-agent autonomous mobility with many agents, congestion effects, and safety constraints by developing a hierarchical decision framework composed of two interdependent layers. The inner layer of the hierarchical decision problem is a very large-scale, path planning and routing problem with uncertainties for which we aim to develop computationally efficient algorithms by combining methods from online and distributed optimization to mediate between decomposed sub-problems where each sub-problem is solved via efficient search-based algorithms or learning-based methods such as multi-agent RL. The layer above, we propose a meta-learning framework for adaptive task assignment, hyper parameter optimization, and sub-problem decomposition each of which feed into the inner layer. Importantly the modeling abstraction for the inner layer is informed by the lower level control decisions individual autonomous agents are making and constraints they face. The outcome will be algorithms with theoretical guarantees and experimental validation via large scale simulations.