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Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

ICLR 2023 · Multi-agent systems and games › Cooperative multi-agent RL · approach: Coordination graphs · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the shared Knowledge Source to the critic (ablation)SAF · HECOGrid cooperative tasksperformanceHelped
Add the shared policy pool (ablation)SAF · HECOGrid tasks with environmental heterogeneityperformanceHelped
Add the shared policy pool (ablation)SAF · HECOGrid tasks requiring high coordinationperformanceNo clear effect
Add the shared policy pool (ablation)QPLEX · HECOGrid environments with 5 agents and high coordinationlearning efficiencyHelped
Train with a shared policy pool (ablation)SAF · TeamTogether, unseen coordination levelsperformanceHelped
Train with a shared policy pool (ablation)SAF · HECOGrid, unseen heterogeneity levelsperformanceHelped
Increase coordination level from 1 to 2IPPO, MAPPO, and SAF · HECOGrid: TeamTogether, TeamSupport, KeyForTreasureperformance: more than 50% decreaseHurt
Increase coordination level to 3IPPO, MAPPO, and SAF · HECOGrid: TeamTogether, TeamSupport, KeyForTreasureperformanceHurt
Increase environmental heterogeneityIPPO, MAPPO, and SAF · HECOGrid: TeamTogether, TeamSupport, KeyForTreasureperformanceHurt
Use SAF instead of IPPO or MAPPOCooperative MARL agents · HECOGrid tasks across coordination and heterogeneity levelsperformanceHelped
Train at coordination level 2 and test at different coordination levelsSAF versus MAPPO · TeamSupportperformanceMixed
Train at coordination level 2 and test at different coordination levelsSAF versus IPPO and MAPPO · TeamTogetherperformanceHelped

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