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...
This work presents a comprehensive study of the application of multi-agent reinforcement learning ( MARL ) based on deep Q-networks ( DQN ), aiming to enhance the cooperation and coordination of multiple agents in complex environments. The core problem addressed is the multi-agent traveling ...
Deep Q-Networkshttps://www.zhihu.com/video/1212178606836805632 03. Experience ReplayExperience Replayhttps://www.zhihu.com/video/1212178754593984512 When the agent interacts with the environment, the sequence of experience tuples can be highly correlated. The naive Q-learning algorithm that learns from...
The multi-agent path planning problem presents significant challenges in dynamic environments, primarily due to the ever-changing positions of obstacles an
These “layers” allow the agent to look at smaller, more reduced versions of data to extract information without an overload. The neural networks are what allow an algorithm to go “deep” and work with large or complex data input more efficiently. Here, we compare Q-learning and Deep Q...
13 min read Does Your Company Have a Data Strategy? Artificial Intelligence This sophistication matrix can show you where you need to go Kate Minogue April 8, 2024 9 min read Speeding Up the Vision Transformer with BatchNorm Deep Learning ...
(as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally. We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage. When playing alongside agents who follow a base-stock policy...
Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels 来自 arXiv.org 喜欢 0 阅读量: 151 作者:Y Yu,SC Liew,T Wang 摘要: This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels...
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 cooperation optimization of multiple agents. Also, considering the controlled characteristics of BES, prioritized experience replay and feasible action...
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more ...