Research map / Multi-agent systems and games / Cooperative multi-agent RL
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PD-DistIAG instead of PDBG or GTD2 | MountainCar policy evaluation · mountaincar | MSPBE convergence speed | Helped |
| Use PD-DistIAG instead of SAGA | MountainCar policy evaluation with ρ > 0 · mountaincar, ρ = 0.01 | MSPBE convergence speed | No clear effect |
| Use PD-DistIAG instead of SAGA | MountainCar policy evaluation with ρ = 0 · mountaincar, ρ = 0 | MSPBE convergence speed | Hurt |
| Use the double-averaging primal-dual updates | PD-DistIAG · decentralized convex-concave saddle-point problems under the stated assumptions | optimality-gap convergence rate: O( σ^t ) | Helped |
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