Skip to Main content Skip to Navigation
Conference papers

Hybrid Mean Field Learning in Large-Scale Dynamic Robust Games

Abstract : One of the objectives in distributed interacting multi-player systems is to enable a collection of different players to achieve a desirable objective. There are two overriding challenges to achieving this objective: The first one is related to the complexity of finding optimal solution. A centralized algorithm may be prohibitively complex when there are large number of interacting players. This motivates the use of adaptive methods that enable players to self-organize into suitable, if not optimal, alternative solutions. The second challenge is limited information. Players may have limited knowledge about the status of other players, except perhaps for a small subset of neighboring players. The limitations in term of information induce robust stochastic optimization, bounded rationality and inconsistent beliefs. In this work, we investigate asymptotic pseudo-trajectories of large-scale dynamic robust games under various COmbined fully DIstributed PAyoff and Strategy Reinforcement Learning (CODIPAS-RL) under outdated noisy measurement and random updates. Extension to continuous action space is discussed.
Complete list of metadata

Cited literature [5 references]  Display  Hide  Download
Contributor : Catherine Magnet Connect in order to contact the contributor
Submitted on : Tuesday, November 22, 2011 - 10:36:49 AM
Last modification on : Monday, December 14, 2020 - 12:38:06 PM
Long-term archiving on: : Thursday, February 23, 2012 - 2:21:28 AM


Files produced by the author(s)


  • HAL Id : hal-00643517, version 1



Hamidou Tembine, Mohamad Assaad. Hybrid Mean Field Learning in Large-Scale Dynamic Robust Games. AMS International Conference on Control and Optimization with Industrial Applications, Aug 2011, Ankara, Turkey. 2 p. ⟨hal-00643517⟩



Les métriques sont temporairement indisponibles