Novel Applications: Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling
in
Workshop: Graph Representation Learning and Beyond (GRL+)
Abstract
We illustrate that developing a theory of ‘how to embed a random graph using GNN’ is the key to achieving the first near-optimal learning-based scheduling algorithm for an NP-hard multi-robot scheduling problem for tasks with time-varying rewards. We focus on a problem referred to as a Multi-Robot Reward Collection (MRRC) problem, of which immediate applications are ridesharing and pickup-and-delivery problems. We 1) observe that states in our robot scheduling problems can be represented as an extension of probabilistic graphical models (PGMs), which we refer to as random PGMs, and 2) develop a meanfield inference method for random PGMs. We then prove that a simple heuristic for applying deep graph encoder for random graph embedding is theoretically justified. We illustrate how a two-step hierarchical inference induces precise Qfunction estimation. We empirically demonstrate that our method achieves near-optimality (plus transferability and scalability, machine scheduling (IPMS) applications in the appendix section). Arxiv preprint: https://arxiv.org/abs/1905.12204.