Mean Field Multi-Agent Reinforcement Learning(MFMARL)是伦敦大学学院(UCL)计算机科学系教授汪军提出的一个多智能体强化学习算法。主要致力于极大规模的多智能体强化学习问题,解决大规模智能体之间的交互及计算困难。由于多智能体强化学习问题不仅有环境交互问题,还有智能体之间的动态影响,因此为了得到最优策略,每个智能...
多智能体强化学习算法MFMARL（Mean Field Multi-Agent Reinforcement Learning）由伦敦大学学院教授汪军提出。该算法主要针对大规模多智能体强化学习问题，通过引入平均场论的思想，简化智能体数量带来的模型空间增大问题。MFMARL算法的实现包括两个主要部分：MF-Q与MF-AC，是对Q-learning和AC算法的改进。理论...
Mean-Field Multi-Agent Reinforcement Learning (MF-MARL) is attractive in the applications involving a large population of homogeneous agents, as it exploits the permutation invariance of agents and avoids the curse of many agents. Most existing results only focus on online settings, in which agents...
oYang,Yaodong, et al. "Mean field multi-agent reinforcement learning."International conference on machine learning. PMLR, 2018. (combined with online RL) o Carmona, René, Mathieu Laurière, and Zongjun Tan. "Model-free mean-field reinforcement learning: mean-field MDP and mean-field Q-learnin...
We present a Reinforcement Learning (RL) algorithm to solve infinite horizon asymptotic Mean Field Game (MFG) and Mean Field Control (MFC) problems. Our approach can be described as a unified two-timescale Mean Field Q-learning: The same algorithm can learn either the MFG or the MFC solution...
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many...
Hence, one often resorts to developing learning algorithms for specific classes of multi-agent systems. In this paper we study reinforcement learning in a specific class of multi-agent systems systems called mean-field games. In particular, we consider learning in stationary mean-field games. We ...
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 ...
Reinforcement learning (RL) is all about an agent learning to make decisions in an environment. The agent takes actions, receives feedback in the form of rewards or penalties, and iteratively adjusts its behavior to maximize cumulative reward. In our case, the agent is the language model, the...
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 ...