Multi-agent deep Q-networks (MADQNs) target to enforce a self-learning softwarization, optimize resource allocation policies, and advocate computation offloading decisions. With gathered network conditions and resource states, the proposed agent aims to explore various actions for estimating expected long...
Specifically, we first adopt the bootstrapped Deep Q-Network (DQN) algorithm to induce exploration via an ensemble of behavior policies, and it outperforms the vanilla DQN in both efficiency and robustness on a handcrafted asymmetric isolated intersection. Further, we develop a multi-agent DQN ...
该论文主要将Lenient应用于在ERM中,并将这一理念扩展到MADRL,并且证明了并行学习隐式协调策略的Lenient-MADRL的agent能够在随机的困难协调任务中收敛于最优的合作策略。 由于目标移动的问题,在单个agent中以往的强化学习算法不适合在多个智能体合作系统中应用。而hysteretic Q-learning 和 leniency算法成功地应用到了MAD...
The multi-agent path planning problem presents significant challenges in dynamic environments, primarily due to the ever-changing positions of obstacles an
Agent Networks:每个智能体都有一个深度Q网络(DQN)或循环神经网络(RNN),用于估计该智能体的局部Q值。 Mixing Network:这个网络负责将所有智能体的Q值以单调的方式混合成全局Q值。混合网络的权重由另一个称为超网络的网络动态生成,这些权重是非负的,确保了Q值的单调组合。
Multi-Agent的Supervisor模式 = 英明指挥官 + 合理分工字节开源的Deep Research 项目属于此种模式。 2.智能体发展趋势:独奏、交响乐团、开放组织 https://www.salesforce.com/blog/the-agentic-ai-era-after-the-dawn-heres-what-to-expect/ Salesforce AI Research 执行副总裁兼首席科学家,在《The Agentic AI ...
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 ...
掌握Multi-Agent实践(三):ReAct Agent集成Bing和Google搜索功能,增强处理复杂任务能力 1.ReActAgent 1.1第一步:准备工具函数 1.2 第二步:创建智能体 1.3 第三步:测试 ReAct 智能体能力 1.4 demo效果展示 2.1 登录谷歌云启动服务 2.2 选择 API 服务 2.3 申请凭据 2.4 Custom Search Engine ID (cx)获取 至此API...
In the field of multi-agent deep reinforcement learning (MADRL), agents can improve the overall learning performance and achieve their objectives by communication. Agents can communicate various types of messages, either to all agents or to specific agent groups, or conditioned on specific ...
This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global $\alpha$-fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly...