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…
Actor-Attention-Critic for Multi-Agent Reinforcement Learning--这是ICML 2019上的一篇关于多智能体强化学习的paper: Actor-Attention-Critic for Multi-Agent Reinforcement Learningarxiv.org/abs/1810.02912 代码地址: https://github.com/shariqiqbal2810/MAACgithub.com/shariqiqbal2810/MAAC 概括:本文通过使...
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...
Multi-agent Reinforcement Learning-Based UAS Control for Logistics Environments we present what is termed the improved Multi-Actor-Attention-Critic (iMAAC) approach, a modified multi-agent reinforcement learning method for application to... H Jo,H Lee,S Jeon,... - Springer, Singapore 被引量: ...
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。
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 Myforkof Multi-agent Particle Environments ...
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 Myforkof Multi-agent Particle Environments ...
There have also been some attention-based methods to infer the relationship between agents. The first one is MAAC [18], which uses a multi-headed attention to learn a centralized critic. AHAC [19] improves the MAAC, and allows the agent to have different attention weights for teammates and ...
Paper tables with annotated results for SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding
文章Actor-Attention-Critic for Multi-Agent Reinforcement Learning, 简称MAAC. Actor-Attention-Critic for Multi-Agent Reinforcement Learningarxiv.org/pdf/1810.02912.pdf 本文算是把soft-ac+attention与多智能体结合起来了,甚至借鉴了COMA的思路,也就是在优势函数评估时候,把其他agent的动作都输入,自己动作不输...