A deep policy iteration method reformulates finite-horizon mean-field games as regenerative problems with deterministic cycles, using particle systems and one-step updates to handle dimensions up to 10,000 efficiently.
On a mean field game approach mod- eling congestion and aversion in pedestrian crowds.Transportation research part B: methodological, 45(10):1572–1589
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Deep Policy Iteration for High-Dimensional Mean-Field Games with Regenerative Reformulation
A deep policy iteration method reformulates finite-horizon mean-field games as regenerative problems with deterministic cycles, using particle systems and one-step updates to handle dimensions up to 10,000 efficiently.