The use of open system architectures and TCP/IP protocols within corporate networks as well as the widespread use of database interfaces are changing the application perspective of groupware. Multi-agent Systems (MAS) have been proposed as a key technology to deal with many of the problems ...
参考 ^abPeter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel: Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. AAMAS 2018: ...
本文属于多智能体强化学习领域,而本文研究的又是其中比较特殊的一类,cooperative MARL,即在这个多智能体环境中,每一个agent 的 reward function 是一样的,所以全体agent 之间是完全的合作关系。首先我们简单复习一下标题中的 advantage function,其实与 single-agent setting下的定义完全一样。Q-value,V-value function...
The use of open system architectures and TCP/IP protocols within corporate networks as well as the widespread use of database interfaces are changing the application perspective of groupware. Multi-agent Systems (MAS) have been proposed as a key technology to deal with many of the problems ...
Bouton M, Farooq H, Forgeat J, Bothe S, Shirazipour M, Karlsson P (2021) Coordinated reinforcement learning for optimizing mobile networks, arXiv:2109.15175 Bowling M, Veloso M (2002) Multiagent learning using a variable learning rate. Artif Intell 136(2):215–250 ...
This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple unmanned aerial vehicles (UAVs), the...
multiagent DRL becomes unstable. In order to guarantee convergence, we design a cooperative multi-agent deep reinforcement learning based framework, which leverages the strategy of centralized training and distributed execution by using locally executable actor networks and fully observable critic networks ...
Unmanned Aerial Vehicle (UAV)-Assisted becomes promising prospects for the development of 6G non-terrestrial networks (NTN) due to their low cost, low latency, flexible coverage, and support for emergency response. UAVs utilize close-range multi-point cooperative computing to provide computational, st...
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find the problem of spurio...
对于每个agenti,Oi是观测空间。 timestept,每个agenti选择一个actionati∈Ai,形成joint actionat:=(at1,…,atN)∈A:=×iAi,根据状态转移函数诱导全局状态的转移P\left(s^{\prime} \mid s, a\right): \mathcal{S} \times \mathcal{A} \times \mathcal{S} \rightarrow[0,1]。N个代理在通过时变网...