- Zinkevich et al., Regret Minimization in Games with Incomplete Information
- Neller et al., An Introduction to Counterfactual Regret Minimization
- Lanctot et al., Monte Carlo Sampling for Regret Minimization in Extensive Games
- Lanctot, Monte Carlo sampling and regret minimization for equilibrium computation and decision-making in large extensive form games
- Lisý et al., Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games
- Farina et al., Stochastic Regret Minimization in Extensive-Form Games
- Waugh et al., Solving Games with Functional Regret Estimation
- Srinivasan et al., Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
- Lockhart et al., Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
- Hennes et al., Neural Replicator Dynamics
- Brown et al., Deep Counterfactual Regret Minimization
- Li et al., Double Neural Counterfactual Regret Minimization
- D'Orazio et al., Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression Counterfactual Regret Minimization
- Perolat et al., From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
- Risk et al., Using Counterfactual Regret Minimization to Create Competitive Multiplayer Poker Agents
- Moravčík et al., DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker
- Brown et al., Superhuman AI for multiplayer poker
- Brown et al., Superhuman AI for multiplayer poker (Supplementary Materials)