Reinforcement Learning Algorithm for Mixed Mean Field Control Games

IF 4.3 3区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Journal of Machine Learning Research Pub Date : 2022-05-04 DOI:10.4208/jml.220915

Andrea Angiuli, Nils Detering, J. Fouque, M. Laurière, Jimin Lin

引用次数: 6

Abstract

We present a new combined \textit{mean field control game} (MFCG) problem which can be interpreted as a competitive game between collaborating groups and its solution as a Nash equilibrium between groups. Players coordinate their strategies within each group. An example is a modification of the classical trader's problem. Groups of traders maximize their wealth. They face cost for their transactions, for their own terminal positions, and for the average holding within their group. The asset price is impacted by the trades of all agents. We propose a three-timescale reinforcement learning algorithm to approximate the solution of such MFCG problems. We test the algorithm on benchmark linear-quadratic specifications for which we provide analytic solutions.

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混合平均场控制博弈的强化学习算法

本文提出了一种\textit{新的组合平均场控制博弈}问题，该问题可以解释为协作群体之间的竞争博弈，其解可以解释为群体之间的纳什均衡。玩家在每个小组中协调他们的策略。一个例子是对经典交易者问题的修正。交易员群体使他们的财富最大化。他们的交易、自己的终端头寸以及集团内的平均持仓都面临成本。资产价格受所有代理人的交易影响。我们提出了一个三时间尺度的强化学习算法来近似求解这类MFCG问题。我们在基准线性二次规范上测试了该算法，并提供了解析解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Machine Learning Research 工程技术-计算机：人工智能

CiteScore

18.80

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.