We propose the Multi-agent Double Deep Q-Networks algorithm, an extension of Deep Q-Networks to the multi-agent paradigm. Two common techniques of multi-agent Q-learning are used to formally describe our proposal, and are tested in a Foraging Task and a Pursuit Game. We also demonstrate ...
3. Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications To describe the MADQNs softwarization framework with the proposed controllers towards virtual resource allocation and eFL aggregation server selection, this section delivers two primary aspects of the proposed scheme, includi...
As far as we know, the Multi-Agent Double Deep Q-Network (MADDQN) has not been previously studied in the literature. The study includes several steps: first, a deep reinforcement learning model based on TimesNet network for stock trading is proposed. Second, the Double DQN (DDQN) algorithm...
The multi-agent path planning problem presents significant challenges in dynamic environments, primarily due to the ever-changing positions of obstacles an
Lenient Multi-Agent Deep Reinforcement Learning 宽大多智能体深度强化学习 Introduction 由于经验重放存储器(ERM)可以对存储的状态进行均匀随机采样来训练深度Q网络,但是在多智能体强化学习(MADRL)中并不适用,因此将宽大(Lenient)应用到了MADRL中,通过(状态-动作对)映射到衰减温度值,让温度值从ERM中采用来更新Lenient...
Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication reinforcement-learningtensorflowdistributeddqnnetworksmulti-agent-system
这篇文章的标题是“Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method”,发表于2024年的VTC-Spring会议,作者包括Siyu Wang、Bo Yang、Zhiwen Yu(均来自西北工业大学计算机科学学院),Xuelin Cao(来自西安电子科技大学网络空间安全学院),Yan Zhang(来自奥斯陆大学信息学部...
reinforcement-learningdeep-reinforcement-learningmultiagent-reinforcement-learning UpdatedNov 6, 2023 Python some Multiagent enviroment in 《Multi-agent Reinforcement Learning in Sequential Social Dilemmas》 and 《Value-Decomposition Networks For Cooperative Multi-Agent Learning》 ...
To address this challenge, a multi-agent deep reinforcement learning framework was proposed to optimize the energy management of the building. In this paper, a dueling double deep Q-network was used for optimization of single agent, and value-decomposition network was put forward to solve the ...
Double deep Q-learning network with dueling architecture The goal of RL is to obtain an optimal policy for an agent (or a robot) to maximize the cumulative reward over time given the initial state, which follows a Markov Decision Process (MDP). Specifically, an agent interacts with the envir...