Research map / Multi-agent systems and games / Cooperative multi-agent RL
Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize local models for multi-step interactions and accumulated prediction error instead of only one-step error | MAMBA on SMAC · SMAC low-data regime | win rate | Helped |
| Use longer planning horizons (ablation) | MAG · SMAC | win rate | Helped |
| Use more random shooting trajectories (ablation) | MAG · SMAC | win rate | Helped |
| Plan for multi-step accumulated error rather than greedily minimizing one-step error | MAG model rollouts · 2c_vs_64zg | accumulative model error | Helped |
| Plan for multi-step accumulated error rather than greedily minimizing one-step error | MAG model rollouts · 2c_vs_64zg | one-step model error | Hurt |
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