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cs.LG2025
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
Jianglin Ding, Jingcheng Tang, Gangshan Jing
Action-dependent individual policies, which incorporate both environmental states and the actions of other agents in decision-making, have emerged as a promising paradigm for achie…
cs.LG2022★ 1 cited
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
Gangshan Jing, He Bai, Jemin George +2
Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent systems (MASs) is challenging because: (i) each agent has access to only limited informati…