MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent
Reinforcement Learning

MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning

17 September 2022

Chuang Gan

Papers citing "MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning"

8 / 8 papers shown

Title
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning Christopher Amato OffRL 31 9 0 04 Sep 2024
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey Jiechuan Jiang Kefan Su Zongqing Lu 38 3 0 10 Jan 2024
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL? Yihe Zhou Shunyu Liu Yunpeng Qing Kaixuan Chen Tongya Zheng Yanhao Huang Jie Song 31 17 0 27 May 2023
Best Possible Q-Learning Jiechuan Jiang Zongqing Lu OffRL 20 5 0 02 Feb 2023
More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization Jiangxing Wang Deheng Ye Zongqing Lu OffRL 39 18 0 26 Sep 2022
Divergence-Regularized Multi-Agent Actor-Critic Kefan Su Zongqing Lu 46 25 0 01 Oct 2021
Multiagent Value Iteration Algorithms in Dynamic Programming and Reinforcement Learning Dimitri Bertsekas 38 38 0 04 May 2020
Bi-level Actor-Critic for Multi-agent Coordination Haifeng Zhang Weizhe Chen Zeren Huang Minne Li Yaodong Yang Weinan Zhang Jun Wang 98 91 0 08 Sep 2019