arXiv:2607. 01525v1 Announce Type: cross Abstract: This monograph provides an introduction to mean field reinforcement learning through the lens of Markov decision processes arising from large-population stochastic control with mean field interactions and common noise.
By Ren\'e Carmona, Mathieu Lauri\`ere
This paper addresses model-free continuous-time mean-field control in a setting where the population dynamics evolve continuously according to an unknown McKean-Vlasov stochastic differential equation, while only discrete-time transition data are available. In the model-based formulation, policy evaluation is naturally described by a stationary Hamilton-Jacobi-Bellman equation on $\mathcal P_2(\mathbb R^d)$, but this equation involves the drift and diffusion coefficients of the controlled McKean-Vlasov dynamics, which are not identifiable when only discrete-time data are available.
The paper presents a mean‑field framework for studying multi‑agent Q‑learning in networked populations, where agents update stateless Q‑values on a fixed graph while the average behavior of the population feeds back to modify the payoff matrix. A deterministic transport equation for the distribution of Q‑values is derived and coupled with a discrete update for the environmental state, and the model is validated against Monte Carlo simulations on several random graph topologies. Results show that the mean‑field system captures macroscopic cooperation dynamics, that environmental feedback reshapes action‑value ordering, and that the timescale of environmental response critically influences learning outcomes.
By Lichen Wang, Shijia Hua, Linjie Liu
arXiv:2607. 08340v1 Announce Type: cross Abstract: Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown.
By Donghwan Lee
arXiv:2606. 26498v1 Announce Type: cross Abstract: This paper addresses model-free continuous-time mean-field control in a setting where the population dynamics evolve continuously according to an unknown McKean-Vlasov stochastic differential equation, while only discrete-time transition data are available.
By Erhan Bayraktar, Martin Hernandez, Qinxin Yan, Yuhua Zhu
arXiv:2605. 16103v2 Announce Type: replace Abstract: Q-learning is known to suffer from overestimation bias: because the Bellman update maximizes noisy or imperfect action-value estimates, positive errors can be selected and propagated, causing learned values to exceed the true optimal values.
By Donghwan Lee