PHASE uses heterogeneous self-play and context-conditioned policies to achieve realistic, zero-shot highway traffic simulation that outperforms traditional rule-based and self-play models on real-world datasets.
A general rein- forcement learning algorithm that masters chess, shogi, and Go through self-play.Science, 362(6419):1140–1144
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Heterogeneous Self-Play for Realistic Highway Traffic Simulation
PHASE uses heterogeneous self-play and context-conditioned policies to achieve realistic, zero-shot highway traffic simulation that outperforms traditional rule-based and self-play models on real-world datasets.