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...
MA-Attention中,每个agent会query其他agent的观测-动作信息,将其作为自己critic的输入,来帮助Q函数学习。 在处理其他agent的观测信息时,使用attention机制,给观测信息赋予权重,来提高学习效率。 critic之间共享参数,MAAC使用联合损失函数来训练critic actor更新函数: 使用counterfactual Baselines解决信用分配问题: 在离线策略中...
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。
Multi-agent deep reinforcement learning with actor-attention-critic for traffic light control: Inspired by this, this paper proposes a multi-agent deep reinforcement learning with actor-attention-critic network for traffic light control (MAAC-TLC) ... B Wang,ZK He,JF Sheng,... - 《Proceedings ...
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 README MIT license Multi-Actor-Attention-Critic Code forActor-Attention-Critic for Multi-Agent Reinforcement Learning(Iqbal and Sha, ICML 2019) Requirements Python 3.6.1 (Minimum) OpenAI baselines, commit hash: 98257ef8c9bd23a24a330731ae54ed086d9ce4a7 ...
Paper tables with annotated results for SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding
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...