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.
arXiv:2407. 04521v3 Announce Type: replace-cross Abstract: This paper studies the continuous-time q-learning in mean-field jump-diffusion models in a setting where the environment simulator does not provide direct access to the population distribution.
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.
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.
arXiv:2607. 11005v1 Announce Type: cross Abstract: This paper develops a model-free reinforcement learning framework for continuous--time extended mean field control problems, where both the dynamics and reward may depend on the joint distribution of states and controls.
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.
arXiv:2606. 20356v1 Announce Type: cross Abstract: In this article, we present a robust $Q$-learning algorithm for discrete-time mean-field control problems under Wasserstein uncertainty in the common noise law.
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.
arXiv:2503. 18607v2 Announce Type: replace-cross Abstract: We introduce the Switching Non-Stationary Markov Decision Process (SNS-MDP) framework, in which the environment transitions among a finite set of MDPs governed by a latent Markov chain while the agent observes only the external state.
The paper proposes a mean‑field reinforcement learning framework that models rewards and transitions as functions of an unknown low‑dimensional aggregate statistic of a large agent population. By learning this low‑dimensional representation in an offline setting, the authors demonstrate a provable method for obtaining near‑optimal policies. Experiments on a one‑step routing game inspired by supply‑chain problems show that, with a fixed neural‑network size and optimization budget, the learned representation improves reward prediction and the quality of Nash equilibria compared to baselines that ignore population structure.
arXiv:2607. 20010v1 Announce Type: new Abstract: In this paper, we present a generalized temporal-difference (TD) reinforcement learning framework based on the theory of conditional expectations.
The paper investigates continuous‑time stochastic control problems with unknown drift and running reward functions, using an exploratory reinforcement learning framework that incorporates relaxed controls and entropy regularization. It develops policy‑iteration algorithms based on probabilistic representations of the optimal value function and its gradient, proving convergence and demonstrating performance through numerical examples. The study also extends to a special case with control‑dependent diffusion, requiring a Hessian representation.
In this paper, we present a generalized temporal-difference (TD) reinforcement learning framework based on the theory of conditional expectations. The value and action-value (Q-value) functions are treated as uncertain quantities, and their estimation is formulated as a stochastic inference problem.