An Introduction to Counterfactual Regret Minimization

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OpenSpiel: A Framework for Reinforcement Learning in Games (2019, neutral): “[31, 63] Counterfactual Regret Minimization (CFR) Tabular [78, 47] (Tabular) Exploitability Tabular [78] External sampling Monte Carlo CFR Tabular [34, 35] Outcome sampling Monte Carlo CFR Tabular [34, 35] Q-learning Tabular [67] Value Iteration Tabular [67] Advantage Actor-Critic (A2C) RL [42] Deep”
A survey and critique of multiagent deep reinforcement learning (2018, methodology): “7 Counterfactual regretminimization is a technique for solving large games based on regretminimization [230, 368] due to a well-known connection between regret and Nash equilibria [39].”
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments (2018, background): “For a thorough introductory tutorial, including backgound and code exercises see [62].”
Report on DeepStack Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker (2019, background): “To get a grasp of the algorithm i will now define notation in a similar manner to [6].”