MAAC是基于actor-critic的[learn to cooperate]算法,该算法利用attention机制改善了MADDPG中critic输入随智能体数目增大而指数增加的扩展性问题,同时还借鉴COMA的思想,利用反事实基线(counterfactual baseline)来区分单个智能体对系统奖励的贡献,另外,MAAC还借鉴了VDN中值函数分解的思想用所有Q网络损失函数之和对每个Q网络进行...
一、研究目标 (一)存在问题 MADDPG无法解决环境不稳定的问题。同时critic的输入是各个智能体的观测-动作,当agent增加时,学习难度增大过快。 (二)研究目标 使用attention解决critic使用全局观察的问题,提高…
MAAC是一种基于actor-critic的多智能体合作学习算法，它结合了MADDPG、COMA、VDN和attention机制，虽然创新性不显著，但它加深了对多智能体协作算法的理解。尽管它可能更适合离散任务，但作者并未充分测试在连续任务中的表现。MAAC的核心是注意力机制，它解决了MADDPG中critic输入随着智能体数量增加而呈指数增...
We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in...
Actor-Attention-Critic for Multi-Agent Reinforcement Learning论文学习笔记,程序员大本营,技术文章内容聚合第一站。
We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in...
Security Insights Additional navigation options master 1Branch 0Tags Code This branch is up to date withshariqiqbal2810/MAAC:master. README MIT license Multi-Actor-Attention-Critic Code forActor-Attention-Critic for Multi-Agent Reinforcement Learning(Iqbal and Sha, ICML 2019) ...
Code forActor-Attention-Critic for Multi-Agent Reinforcement Learning(Iqbal and Sha, ICML 2019) Requirements 这些版本只是我所使用的,不一定是严格的要求。 Python 3.6.1 (Minimum) OpenAI baselines, commit hash: 98257ef8c9bd23a24a330731ae54ed086d9ce4a7 ...
LSTM在深度学习过程中经常用到,因为他可以选择性的忽略掉前面的不重要的信息而保留重要的信息,在学习“Factual” or “Emotional”: Stylized image captioning with adaptive learning and attention这篇论文时,其核心模块 style-factual LSTM定义如下: 如果只看公式并看不出所以然,因此想要搞懂这个公式的含义,就需要深...
Multi-Actor-Attention-Critic MAAC 首先,对于智能体i的Q值,定义如下: f_{i},g_{i}是MLP,x_{i}是其他智能体状态信息attention后的特征,如上图黄色那一块运算,做attention不包括自身。也即是: 同时作者也采用了multi-head方式。所有对于多智能体的critic优化如下,所有的智能体可以共享一个attention网络, Q可以...