Mean-Field Reinforcement Learning without Synchrony

• URL: http://arxiv.org/abs/2602.18026v1
• Date: Fri, 20 Feb 2026 06:42:08 GMT
• Title: Mean-Field Reinforcement Learning without Synchrony
• Authors: Shan Yang,
• Abstract summary: Mean-field reinforcement learning scales to large populations by reducing each agent's dependence on others to a single summary statistic -- the mean action.<n>Existing MF-RL theory is built on the mean action and does not extend to $$.<n>We construct the Temporal Mean Field framework around the population distribution $$ from scratch, covering the full spectrum from fully synchronous to purely sequential decision-making.
• Score: 11.907264672363718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mean-field reinforcement learning (MF-RL) scales multi-agent RL to large populations by reducing each agent's dependence on others to a single summary statistic -- the mean action. However, this reduction requires every agent to act at every time step; when some agents are idle, the mean action is simply undefined. Addressing asynchrony therefore requires a different summary statistic -- one that remains defined regardless of which agents act. The population distribution $μ\in Δ(\mathcal{O})$ -- the fraction of agents at each observation -- satisfies this requirement: its dimension is independent of $N$, and under exchangeability it fully determines each agent's reward and transition. Existing MF-RL theory, however, is built on the mean action and does not extend to $μ$. We therefore construct the Temporal Mean Field (TMF) framework around the population distribution $μ$ from scratch, covering the full spectrum from fully synchronous to purely sequential decision-making within a single theory. We prove existence and uniqueness of TMF equilibria, establish an $O(1/\sqrt{N})$ finite-population approximation bound that holds regardless of how many agents act per step, and prove convergence of a policy gradient algorithm (TMF-PG) to the unique equilibrium. Experiments on a resource selection game and a dynamic queueing game confirm that TMF-PG achieves near-identical performance whether one agent or all $N$ act per step, with approximation error decaying at the predicted $O(1/\sqrt{N})$ rate.

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