TACTIC: Task-Aware Sparse Coordination Graphs for Multi-Task Multi-agent Reinforcement Learning

Value factorization eases non-stationarity in MARL, but its static coordination assumptions hinder generalization on long-horizon tasks with shifting dependencies. Prior VQ-VAE methods abstract trajectories yet miss time-varying inter-agent dependencies. We present TACTIC, a CTDE framework with three components: (i) VQ-VAE-based trajectory abstraction that learns discrete task-semantic classes; (ii) semantic-conditioned sparse coordination graphs that adapt dependencies by pruning edges according to variance-based pairwise payoff sensitivity; and (iii) a pretrained, frozen trajectory-class predictor that conditions local policies while decoupling task recognition from control. On SMAC and SUMO, TACTIC shows strong overall competitiveness and adaptive coordination under sparse rewards and dynamic task structures.