Research map / Optimization / Nonconvex and smooth optimization
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use nested-gradient policy updates (Gauss-Newton, natural, vanilla) for the maximizer | zero-sum LQ game policy optimization · Case 1 simulation | convergence to Nash equilibrium cost | Helped |
| run the methods without Assumption 2.1 ii) (no saddle-point guarantee) | nested-gradient, alternating-gradient, GDA · Case 2 simulation | convergence to NE | No clear effect |
| remove the projection onto Omega (ablation) | projected nested-gradient · Cases 1 and 2 simulation | convergence to NE | No clear effect |
| replace outer-loop exact inner solution (nested) by finite inner iterations (alternating) or simultaneous updates (GDA) (ablation) | policy optimization for LQ games · Case 2 simulation | convergence to NE | No clear effect |
Closest papers by meaning
- Solving Zero-Sum Convex Markov Games (ICML 2025 · similarity 0.91)
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- On the convergence of policy gradient methods to Nash equilibria in general stochastic games (NeurIPS 2022 · similarity 0.91)
- Can We Find Nash Equilibria at a Linear Rate in Markov Games? (ICLR 2023 · similarity 0.90)
- Solving Zero-Sum Markov Games with Continuous State via Spectral Dynamic Embedding (NeurIPS 2024 · similarity 0.89)
- A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence (ICLR 2025 · similarity 0.88)
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