DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies
Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something conventional neural policies struggle to achieve.
ProofPaper ↗
Key points
- Diffusion models offer a natural solution, but standard diffusion training requires direct samples from a fixed target distribution, which are generally unavailable under iterative game-solving updates.
- We propose a diffusion-based framework for equilibrium computation that enables diffusion-based variants of Online Mirror Descent (OMD) and Fictitious Play (FP).
- Our approach derives a reweighted score-matching objective that trains diffusion policies without requiring direct samples from each updated policy.
- We evaluate our approach on forest protection games and continuous Colonel Blotto games, reducing the Nash gap over the strongest baseline by 35.6% and 24.7%, respectively, with minimal hyperparameter tuning.
Sources (1)
- [1]DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion PoliciesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 09:33 PM
Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something conventional neural policies struggle to achieve.
Diffusion models offer a natural solution, but standard diffusion training requires direct samples from a fixed target distribution, which are generally unavailable under iterative game-solving updates.
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