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Research map / Multi-agent systems and games / Cooperative multi-agent RL

FACMAC: Factored Multi-Agent Centralised Policy Gradients

NeurIPS 2021 · Multi-agent systems and games › Cooperative multi-agent RL · approach: Decentralized actor-critic MARL · 104 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
FACMAC (factored critic + centralised policy gradient) instead of MADDPGmulti-agent actor-critic · Continuous Predator-Prey and MAMuJoComean episode returnHelped
FACMAC instead of MADDPGmulti-agent actor-critic · SMAC (6 maps)test win rateMixed
FACMAC instead of QMIX (same factorisation)SMAC · SMAC MMM, bane_vs_bane, MMM2, 27m_vs_30mtest win rateHelped
FACMAC instead of COMIXcontinuous MARL · Continuous Predator-Prey and MAMuJoComean episode returnNo clear effect
factor the critic (FACMAC without CPG vs MADDPG) (ablation)MADDPG · SMAC 2c_vs_64zg, MMM2test win rateHelped
add centralised policy gradient (ablation)FACMAC · SMAC MMM2test win rateHelped
add centralised policy gradient (ablation)FACMAC and MADDPG · ManyAgent Swimmer 2 agentsmean episode returnNo clear effect
add centralised policy gradient (ablation)FACMAC · ManyAgent Swimmer 10 agentsmean episode returnHelped
add centralised policy gradient (ablation)MADDPG · Continuous Matrix Gamemean test returnHelped
replace nonlinear mixing with VDN sum (FACMAC-vdn) (ablation)FACMAC · MAMuJoCo Humanoid, ManyAgent Swimmermean episode returnHurt
replace nonlinear mixing with VDN sum (FACMAC-vdn) (ablation)FACMAC · Continuous Predator-Prey many agentsmean episode returnHelped
use nonmonotonic factorised critic (ablation)FACMAC · nonmonotonic matrix game / predator-preyreturnHelped

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