mlsanityData to 14 Oct 2026

DREAM: Deep Regret minimization with Advantage baselines and Model-free learning

arXiv 2006.10410 ↗First posted 18 Jun 2020cs.LG cs.GT stat.MLcited by 36

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Abstract (arXiv)

We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash Equilibrium in two-player zero-sum games and to an extensive-form coarse correlated equilibrium in all other games. Our primary innovation is an effective algorithm that, in contrast to other regret-based deep learning algorithms, does not require access to a perfect simulator of the game to achieve good performance. We show that DREAM empirically achieves state-of-the-art performance among model-free algorithms in popular benchmark games, and is even competitive with algorithms that do use a perfect simulator.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jun 2020.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 7; 2024: 2; 2025: 5; 2026: 7.

36 papers.

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  1. 2022A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Gamescited by 66
  2. 2021XDO: A Double Oracle Algorithm for Extensive-Form Gamescited by 34
  3. 2024A Survey on Self-play Methods in Reinforcement Learningcited by 26
  4. 2022ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regretcited by 24
  5. 2021Student of Games: A unified learning algorithm for both perfect and imperfect information gamescited by 23
  6. 2022The Power of Regularization in Solving Extensive-Form Gamescited by 21
  7. 2021Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Gamescited by 15
  8. 2022PerfectDou: Dominating DouDizhu with Perfect Information Distillationcited by 15
  9. 2025Reevaluating Policy Gradient Methods for Imperfect-Information Gamescited by 13
  10. 2022Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Gamescited by 11
  11. 2021Search in Imperfect Information Gamescited by 8
  12. 2021Adversarial Deep Learning for Online Resource Allocationcited by 7
24 more
  1. 2023Deep Reinforcement Learning for Autonomous Cyber Defence: A Surveycited by 7
  2. 2024A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergencecited by 5
  3. 2021CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Spacecited by 4
  4. 2023Hierarchical Deep Counterfactual Regret Minimizationcited by 4
  5. 2021Learning to Be Cautiouscited by 3
  6. 2022Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chesscited by 3
  7. 2023Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposabilitycited by 3
  8. 2023JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Gamescited by 2
  9. 2023Local and adaptive mirror descents in extensive-form gamescited by 2
  10. 2023Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learningcited by 2
  11. 2025Look-ahead Reasoning with a Learned Model in Imperfect Information Gamescited by 2
  12. 2025NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibriacited by 2
  13. 2021D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Networkcited by 1
  14. 2025Deep (Predictive) Discounted Counterfactual Regret Minimizationcited by 1
  15. 2026AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Playcited by 1
  16. 2026Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Gamescited by 1
  17. 2026EMAgnet: Parameter-Space EMA Regularization for Policy Gradient Self-Play in Large Gamescited by 1
  18. 2026How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctionscited by 1
  19. 2022Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Correctionscited by 0
  20. 2023Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbationscited by 0
  21. 2025KrwEmd: Revising the Imperfect-Recall Abstraction from Forgetting Everythingcited by 0
  22. 2026Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibriacited by 0
  23. 2026NashDreamer: Model-Based Reinforcement Learning for Zero-Sum Imperfect-Information Gamescited by 0
  24. 2026Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learnerscited by 0