Abstract

Multi-agent reinforcement learning (MARL) has achieved strong results on large-scale decision making, yet most methods are model-free, limiting sample efficiency and making coordination harder as teammates' policies evolve during training. Model-based reinforcement learning can reduce data usage, but planning and search scale poorly with joint action spaces. We introduce MACT, a decentralized action-conditioned Transformer world model for long-horizon cooperative control. Each agent processes discretized observation-action tokens with a shared Transformer, while a single cross-agent Perceiver step provides global context under centralized training and decentralized execution. MACT targets long-horizon coordination by coupling Perceiver-derived global context with an action-conditioned contrastive objective that predicts future latent representations over a short horizon conditioned on planned actions. On the StarCraft Multi-Agent Challenge, MACT achieves the strongest aggregate performance among 6 model-free and model-based baselines under matched low-data training budgets. Across 12 maps, including 2 SuperHard scenarios, MACT attains the best mean and median win rates, and ablations show that multi-step prediction and per-agent action conditioning are central to its gains.