Research map / Multi-agent systems and games / Cooperative multi-agent RL
Sequential Asynchronous Action Coordination in Multi-Agent Systems: A Stackelberg Decision Transformer Approach
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Proposes STEER, a dual-Transformer PPO-based MARL method that encodes Stackelberg sequential decision-making for asynchronous action coordination, with knowledge distillation to decentralized student policies.
Problem
Asynchronous action coordination among agents in mixed and cooperative multi-agent systems; existing Stackelberg-game MARL methods are limited to shared global states and sequential, costly heterogeneous policy updates.
Approach
Aligns the hierarchical leader-follower decision structure of Stackelberg games with autoregressive Transformers: an Inner Transformer Block encodes state/observations and an Outer causal Transformer Block generates each agent's action in priority order, trained end-to-end with PPO so all agent policies update in parallel; a distilled MLP student enables decentralized execution.
Key results
- On matrix games STEER converged to the optimal strategy in 100% of trials on Penalty (k=0 and k=-100) and Mixing, and 72% on Penalty k=-1000, while MAT, HAPPO and MAPPO often failed.
- In GRF, the decentralized student network achieved performance close to STEER (e.g., 0.9426 vs 0.9339 on academy_pass_and_shoot_with_keeper).
- STEER outperformed MAPPO, HAPPO, MAT and STEP across GRF, MA-MuJoCo and HORM and trained faster by parallel updates.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use STEER instead of STEP | Matrix game scenarios · Penalty k=-1000 | convergence to optimal strategy: 44% -> 72% | Helped |
| Use STEER instead of STEP | Matrix game scenarios · Penalty k=-100 | convergence to optimal strategy: 93% -> 100% | Helped |
| Use STEER instead of STEP | Matrix game scenarios · Coordination | convergence to optimal strategy: 94% -> 95% | Helped |
| Use STEER instead of STEP | Matrix game scenarios · Cooperation | convergence to optimal strategy: 90% -> 96% | Helped |
| Use STEER instead of STEP | Matrix game scenarios · Mixing | convergence to optimal strategy: 100% -> 100% | No clear effect |
| Replace the Inner Transformer Block with an MLP (ablation) | STEER · Matrix game scenarios | performance | No clear effect |
| Replace the Inner Transformer Block with an MLP (ablation) | STEER · MA-MuJoCo | performance | Hurt |
| Replace the Outer Transformer Block with a GRU (ablation) | STEER · Matrix game scenarios | performance | Hurt |
| Use only the ITB output to fit policy and value functions (ablation) | STEER · Matrix game scenarios | performance | Hurt |
| Use only the OTB output to fit policy and value functions (ablation) | STEER · Complex scenarios | performance | Hurt |
| Change the predefined agent priority ordering (ablation) | STEER · GRF | performance | No clear effect |
| Distill STEER into decentralized student networks | STEER · GRF | performance | Mixed |
Surprising findings
- The student network sometimes marginally surpassed the centralized STEER teacher in GRF.
- MAT, HAPPO and MAPPO converged to the sub-optimal Nash equilibrium in the Penalty game for k<0, while STEER and STEP did not.
Limitations
- STEER is centralized; decentralized execution relies on a separate knowledge distillation step.
- The predefined priority order may be suboptimal; adaptive priority determination is left to future work.
- Evaluation uses a limited set of environments and baselines.
Experimental setup
Datasets: 1-step matrix game n-step matrix game MA-MuJoCo Google Research Football Highway On-Ramp Merging
Benchmarks: Penalty matrix game Mixing matrix game Coordination matrix game Cooperation matrix game MA-MuJoCo Google Research Football Highway On-Ramp Merging
Metrics: average return convergence percentage to optimal strategy over 100 trials wall clock training time
Baselines: MAPPO HAPPO MAT STEP
Builds on: PPO Decision Transformer MAT STEP HAPPO
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