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...
The study of multi-agent reinforcement learning can solve many problems in real life. The current research can be divided into two aspects: one is adding the information of other agents into the critic-network to form a global critic-network, as MADDPG; the other is putting them into the ...
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...
Chen, M., Li, Y., Wang, E., Yang, Z., Wang, Z., Zhao, T.: Pessimism meets invariance: provably efficient offline mean-field multi-agent rl. Adv. Neural Inform. Process. Syst. 34, 17913–17926 (2021) Google Scholar Chen, Y., Liu, J., Khoussainov, B.: Maximum entropy inver...
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 by simply tuning the ratio of two learning parameters. The algorithm is in discrete time and space where the agent not only provides an action to...
Multi-Agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions across agents. In this article, we propose a new method based on local communication learning to tackle the multi-agent RL...
Difference RL Agent training plot and result plot 0 답변 Multi-Agent Reinforcement learning 1 답변 Reinforcement learning action getting saturated at one range of values 1 답변 전체 웹사이트 ARS in MATLAB File Exchange Multi-Agent Formation Control ...
Mean-Field Control (MFC) has recently been proven to be a scalable tool to approximately solve large-scale multi-agent reinforcement learning (MARL) problems. However, these studies are typically limited to unconstrained cumulative reward maximization framework. In this paper, we show that one can ...
Finally, we discuss extensions to heterogeneous (non-symmetric) risk-sensitive mean field games. Author information Authors and Affiliations School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea...
Keywords: reinforcement learning; mean-field game; equilibrium 1. Introduction 1.1. Motivation We live in a world where multiple agents interact repeatedly in a common environment. For example, multiple robots interact to achieve a specific goal. Multi-agent reinforcement learning (MARL) refers to th...