On The Convergence Of Model Free Learning In Mean Field Games

Abstract

Learning by experience in Multi-Agent Systems (MAS) is a difficult and exciting task, due to the lack of stationarity of the environment, whose dynamics evolves as the population learns. In order to design scalable algorithms for systems with a large population of interacting agents (e.g. swarms), this paper focuses on Mean Field MAS, where the number of agents is asymptotically infinite. Recently, a very active burgeoning field studies the effects of diverse reinforcement learning algorithms for agents with no prior information on a stationary Mean Field Game (MFG) and learn their policy through repeated experience. We adopt a high perspective on this problem and analyze in full generality the convergence of a fictitious iterative scheme using any single agent learning algorithm at each step. We quantify the quality of the computed approximate Nash equilibrium, in terms of the accumulated errors arising at each learning iteration step. Notably, we show for the first time convergence o

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyelie2019on

Related papers

• A Single Online Agent Can Efficiently Learn Mean Field Games (2024)0.00
• Mean Field Multi-agent Reinforcement Learning (2018)2.26
• Decentralized Mean Field Games (2021)3.58
• Generalization In Mean Field Games By Learning Master Policies (2021)7.81
• Oracle-free Reinforcement Learning In Mean-field Games Along A Single Sample Path (2022)0.00
• A General Framework For Learning Mean-field Games (2020)0.00
• Networked Communication For Decentralised Agents In Mean-field Games (2023)0.00
• Reinforcement Learning For Mean Field Games With Strategic Complementarities (2020)0.00