@inproceedings{
barnes2023massively,
title={Massively Scalable Inverse Reinforcement Learning in Google Maps},
author={Matt Barnes and Matthew Abueg and Oliver F. Lange and Matt Deeds and Jason Trader and Denali Molitor and Markus Wulfmeier and Shawn O'Banion},
booktitle={NeurIPS 2023 Workshop on Generalization in Planning},
year={2023},
url={https://openreview.net/forum?id=zqcEQVzvZx}
}
Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and demonstration trajectories. In this paper, we introduce scaling techniques based on graph compression, spatial parallelization, and improved initialization conditions inspired by a connection to eigenvector algorithms. We revisit classic IRL methods in the routing context, and make the key observation that there exists a trade-off between the use of cheap, deterministic planners and expensive yet robust stochastic policies. This insight is leveraged in Receding Horizon Inverse Planning (RHIP), a new generalization of classic IRL algorithms that provides fine-grained control over performance trade-offs via its planning horizon. Our contributions culminate in a policy that achieves a 16-24% improvement in route quality at a global scale, and to the best of our knowledge, represents the largest published benchmark of IRL algorithms in a real-world setting to date. We conclude by conducting an ablation study of key components, presenting negative results from alternative eigenvalue solvers, and identifying opportunities to further improve scalability via IRL-specific batching strategies.
Massively Scalable Inverse Reinforcement Learning in Google Maps
Matt Barnes Matthew Abueg Oliver F. Lange Matt Deeds Jason Trader Denali Molitor Markus Wulfmeier Shawn O'Banion