Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach Learning the behavior of large agent populations is an important task for numerous research areas. Although the field of multi-agent reinforcement learning... C Fabian,K Cui,H Koeppl 被引量: 0发表: 2024年 Mean field ...
We study reinforcement learning in mean-field games. To achieve the Nash equilibrium, which consists of a policy and a mean-field state, existing algorithms require obtaining the optimal policy while fixing any mean-field state. In practice, however, the policy and the mean-field state evolve ...
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New approach: Deep Galerkin for mean field games with non-separable Hamiltonians.We prove the convergence of our NN approximation with a single hidden layer.Our method handles up to 300+ dimensions with a single layer for faster computing.Efficiency demonstrated against prior approaches and traffic ...
The recent mean field game (MFG) formalism has enabled the application of inverse reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of inferring reward signals that can explain demonstrated behaviours of large populations. The existing IRL methods for MFGs are ...
Mean Field Games Mean Field-Games(MFG)are a relatively new area in the game theory space. The MFG theory was just developed in 2006 as part of a series of independent papers published by Minyi Huang, Roland Malhamé and Peter Caines in Montreal, and by Jean-Michel Lasry and Fields medalis...
Learning While Playing in Mean-Field Games: Convergence and Optimality We study reinforcement learning in mean-field games. To achieve the Nash equilibrium, which consists of a policy and a mean-field state, existing algorithms require obtaining the optimal policy while fixing any mean-field state....
anticipatedapplicationsofMFGandMFCmodelsthatarebeyondreachwithexistingnumericalmethods.meanfieldgames|meanfieldcontrol|machinelearning|optimaltransport|Hamilton–Jacobi–BellmanequationsMeanfieldgames(MFG)(1–5)andmeanfieldcontrol(MFC)(6)allowonetosimulateandanalyzeinterac-tionswithinlargepopulationsofagents....
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, and Tuo Zhao. NeurIPS, 2021. Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning Yiqin Yang, Xiaoteng Ma...
200210 Liberty or Depth #mean_field 200514 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors #ensemble #variational_inference benchmark 230720 SciBench 230807 AgentBench bert 200305 What the [MASK] 200405 FastBERT #distillation #lightweight 200408 DynaBERT #distillation #pruning 200412 ...