Scalable Inverse Reinforcement Learning in Google Maps

The author introduces scaling techniques for IRL algorithms to address planetary-scale problems, culminating in a policy that significantly improves route quality at a global scale. The key insight is the trade-off between cheap deterministic planners and expensive stochastic policies.