Multi-Agent Determinantal Q-Learning 长颈鹿骑着鲨鱼 Decide anything.36 人赞同了该文章 本文提出了一个基于 CTDE 框架的、用以解决 Dec-POMDP 问题的 Value-based MARL 算法 Q-DPP 以及其深度版本 Deep Q-DPP,被 ICML 2020 接收为 Poster。 图1:Q-DPP 方法定位。可以看出,Q-DPP 是属于 CTDE 框架的...
Multi-Agent Determinantal Q-LearningDavid MguniJun WangKun ShaoLiheng ChenWeinan ZhangYaodong YangYing Wen
在正常形式的博弈(normal-form game)中,NE表示一个联合策略的平衡点,其中每个agent根据相对于其他agent的最佳反应(best response)来行动。最佳反应通过考虑其他所有agent的策略来获得最佳汇报。由于最佳反应取决于与其他agent的相对奖励,agent所获得的绝对奖励是不重要的,换句话说,对所有玩家的奖励进行正的仿射变换是不改...
Q-DPP is a novel function approximator for cooperative multi-agent reinforcement learning problems based on determinantal point process (DPP). Q-DPP promotes agents to acquire diverse behavioral models; this allows a natural factorization for the joint Q-functions with no need for a priori structur...
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction
Independent LearningIQL:Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agentshttps://github.com/oxwhirl/pymarlICML1993 Value DecompositionVDN:Value-Decomposition Networks For Cooperative Multi-Agent Learninghttps://github.com/oxwhirl/pymarlAAMAS2017 ...
图片摘自 Multi-agent Determinantal Q-Learning 在上图中,作者们通过 exploration 和 centralized/independent 来对一众算法进行分类,而在这篇文章中作者们主要着眼于offline MARL,即 learner 没有办法与环境进行实时地互动,只能对既有的数据集进行充分的利用。对于 multi-agent 的 actor-critic 算法,主要可以分为两类...
In [14], a deep reinforcement learning-based algorithm was proposed to solve the UAV trajectory planning, mission scheduling, and deployment in complex regional scenarios. To facilitate the modeling and analysis of wireless geometric networks, PPP is widely used because of its ease of handling. In...