Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling

Jul 17, 2020

Speakers

About

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.

Organizer

Categories

About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

Store presentation

Should this presentation be stored for 1000 years?

How do we store presentations

Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

Sharing

Recommended Videos

Presentations on similar topic, category or speaker