Double Oracle Algorithm for Computing Equilibria in Continuous Games
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Abstract (arXiv)
Many efficient algorithms have been designed to recover Nash equilibria of various classes of finite games. Special classes of continuous games with infinite strategy spaces, such as polynomial games, can be solved by semidefinite programming. In general, however, continuous games are not directly amenable to computational procedures. In this contribution, we develop an iterative strategy generation technique for finding a Nash equilibrium in a whole class of continuous two-person zero-sum games with compact strategy sets. The procedure, which is called the double oracle algorithm, has been successfully applied to large finite games in the past. We prove the convergence of the double oracle algorithm to a Nash equilibrium. Moreover, the algorithm is guaranteed to recover an approximate equilibrium in finitely-many steps. Our numerical experiments show that it outperforms fictitious play on several examples of games appearing in the literature. In particular, we provide a detailed analysis of experiments with a version of the continuous Colonel Blotto game.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 1; 2025: 1.
7 papers.
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- 2025Robust Optimization with Diffusion Models for Green Security19 Feb 2025
- 2024Games for AI Control: Models of Safety Evaluations of AI Deployment Protocols12 Sep 2024
- 2023ApproxED: Approximate exploitability descent via learned best responses20 Jan 2023
- 2022Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks29 Nov 2022
- 2022Fail-Safe Adversarial Generative Imitation Learning3 Mar 2022
- 2021No-Press Diplomacy from Scratch6 Oct 2021
- 2021Robust Reinforcement Learning Under Minimax Regret for Green Security15 Jun 2021