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.
Policy gradient and actor-critic learning in continu- ous time and space: Theory and algorithms.Journal of Machine Learning Research, 23(275):1–50, 2022
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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.