Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games

• URL: http://arxiv.org/abs/2605.14379v1
• Date: Thu, 14 May 2026 05:00:52 GMT
• Title: Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games
• Abstract summary: Finding approximate equilibria for large-scale imperfect-information competitive games such as StarCraft, Dota, and CounterStrike remains computationally infeasible.<n>We propose a multi-agent starting-state sampling strategy designed to substantially accelerate online exploration.<n>We show that augmenting starting state distributions when solving imperfect information games can lead to biased equilibria.
• Score: 9.1095646170628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding approximate equilibria for large-scale imperfect-information competitive games such as StarCraft, Dota, and CounterStrike remains computationally infeasible due to sparse rewards and challenging exploration over long horizons. In this paper, we propose a multi-agent starting-state sampling strategy designed to substantially accelerate online exploration in regularized policy-gradient game methods for two-player zero-sum (2p0s) games. Motivated by an assumption that offline demonstrations from skilled humans can provide good coverage of high-level strategies relevant to equilibrium play, we propose the initialization of reinforcement learning data collection at intermediate states sampled from offline data to facilitate exploration of strategically relevant subgames. Referring to this method as Data-Augmented Game Starts (DAGS), we perform experiments using synthetic datasets and analytically tractable, long-horizon control variants of two-player Kuhn Poker, Goofspiel, and a counterexample game designed to penalize biased beliefs over hidden information. Under fixed computational budgets, DAGS enables regularized policy gradient methods to achieve lower exploitability in games with significantly more challenging exploration. We show that augmenting starting state distributions when solving imperfect information games can lead to biased equilibria, and we provide a straightforward mitigation to this in the form of multi-task observation flags. Finally, we release a new set of benchmark environments that drastically increase exploration challenges and state counts in existing OpenSpiel games while keeping exploitability measurements analytically tractable.

Related papers

• Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning [6.299504742623642]
  We consider the problem in a mixed-motive multiagent setting, where the goal is to solve a game under the offline learning constraint.<n>We extend Policy Space Response Oracles (PSRO), an online game-solving approach, by quantifying game dynamics uncertainty.<n>We propose a novel meta-strategy solver, tailored for the offline setting, to guide strategy exploration in PSRO.
  arXiv  Detail & Related papers  (2026-02-27T23:24:02Z)
• Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents [56.25101378553328]
  We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned keyboard-mouse inputs.<n>Game-TARS is pre-trained on over 500B tokens with diverse trajectories and multimodal data.<n> Experiments show that Game-TARS achieves about 2 times the success rate over the previous sota model on open-world Minecraft tasks.
  arXiv  Detail & Related papers  (2025-10-27T17:43:51Z)
• Online Competitive Information Gathering for Partially Observable Trajectory Games [24.25139588281181]
  Game-theoretic agents must make plans that optimally gather information about their opponents.<n>We formulate a finite history/horizon refinement of POSGs which admits competitive information gathering behavior in trajectory space.<n>We present an online method for computing rational trajectory plans in these games which leverages particle-based estimations of the state space and performs gradient play.
  arXiv  Detail & Related papers  (2025-06-02T17:45:58Z)
• Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration [54.8229698058649]
  Unlabeled offline trajectory data can be leveraged to learn efficient exploration strategies.<n>Our method SUPE consistently outperforms prior strategies across a suite of 42 long-horizon, sparse-reward tasks.
  arXiv  Detail & Related papers  (2024-10-23T17:58:45Z)
• Accelerating Exploration with Unlabeled Prior Data [66.43995032226466]
  We study how prior data without reward labels may be used to guide and accelerate exploration for an agent solving a new sparse reward task. We propose a simple approach that learns a reward model from online experience, labels the unlabeled prior data with optimistic rewards, and then uses it concurrently alongside the online data for downstream policy and critic optimization.
  arXiv  Detail & Related papers  (2023-11-09T00:05:17Z)
• Data-Scarce Identification of Game Dynamics via Sum-of-Squares Optimization [29.568222003322344]
  We introduce the Side-Information Assisted Regression (SIAR) framework, designed to identify game dynamics in multiplayer normal-form games. SIAR is solved using sum-of-squares (SOS) optimization, resulting in a hierarchy of approximations that provably converge to the true dynamics of the system. We showcase that the SIAR framework accurately predicts player behavior across a spectrum of normal-form games, widely-known families of game dynamics, and strong benchmarks, even if the unknown system is chaotic.
  arXiv  Detail & Related papers  (2023-07-13T09:14:48Z)
• Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks [83.28949556413717]
  We study the problem of computing an approximate Nash equilibrium of continuous-action game without access to gradients. We model players' strategies using artificial neural networks. This paper is the first to solve general continuous-action games with unrestricted mixed strategies and without any gradient information.
  arXiv  Detail & Related papers  (2022-11-29T05:16:41Z)
• Efficient exploration of zero-sum stochastic games [83.28949556413717]
  We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay. During a limited-duration learning phase, the algorithm can control the actions of both players in order to try to learn the game and how to play it well. Our motivation is to quickly learn strategies that have low exploitability in situations where evaluating the payoffs of a queried strategy profile is costly.
  arXiv  Detail & Related papers  (2020-02-24T20:30:38Z)
• From Poincar\'e Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization [49.368421783733815]
  We show how adapting the reward can give strong convergence guarantees in monotone games. We also show how this reward adaptation technique can be leveraged to build algorithms that converge exactly to the Nash equilibrium.
  arXiv  Detail & Related papers  (2020-02-19T21:36:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.