文章链接:Multi-Agent Reinforcement Learning is a Sequence Modeling Problem 文章总结 背景 这篇文章着眼于多智能体强化学习(MARL)的问题,希望通过引入序列模型(SM)来解决这一领域的挑战。 创新点 提出了一种新的解决协作MARL问题的通用框架,将其统一为类似Transformer的编码器-解码器模型。 利用多智能体优势分解定理...
文献阅读:Multi-Agent Reinforcement Learning is A Sequence Modeling Problem 城北 3 人赞同了该文章 一、研究目的 1、原有方法:HAPPO、MAPPO等方法 2、原有方法缺点:这些方法并没有解决多智能体之间相互作用的问题 3、研究目标:使用sequence model(SM)对MARL问题进行建模,提出一个新的MARL训练范式。核心是多智能...
Multi-Agent Reinforcement Learning is A Sequence Modeling Problem
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of...
Applications of Multi-Agent Deep Reinforcement Learning Communication in Network Management: A Survey With the advancement of artificial intelligence technology, the automation of network management, also known as Autonomous Driving Networks (ADN), is gaini... Y Pi,W Zhang,Y Zhang,... 被引量: 0...
Learn what multi-agent reinforcement learning is and some of the challenges it faces and overcomes. You will also learn what an agent is and how multi-agent systems can be both cooperative and adversarial. Be walked through a grid world example to highlight some of the benefits of both dec...
我们将多场景的排序问题看成一个完全合作的、部分可观测的多智能体序列决策问题,利用Multi-Agent Reinforcement Learning的方法来尝试着对问题进行建模。该模型以各个场景为Agent,让各个场景不同的排序策略共享同一个目标,同时在一个场景的排序结果会考虑该用户在其他场景的行为和反馈。也就是,不同场景不同的action,...
多智能体强化学习是一个序列建模问题(Multi-Agent Reinforcement Learning is A Sequence Modeling Problem) 落影 风起于青萍之末10 人赞同了该文章 1、 Abstract 大序列模型 (SM) 如GPT 系列和 BERT 在自然语言过程、视觉和最近的强化学习中表现出了出色的性能和泛化能力。一个自然的后续问题是如何将多智能体决...
A model used for velocity control during car following is proposed based on reinforcement learning (RL). To optimize driving performance, a reward function... M Zhu,Y Wang,Z Pu,... - 《Transportation Research Part C Emerging Technologies》 被引量: 0发表: 2020年 Multi-Agent Deep Reinforcemen...
Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition of multiagent systems. However, most research on multiagent systems applying a reinforcement learning algorithm, focus on a method to reduce complexity due to the existence of multiple agents and goals....