Student of Games: A unified learning algorithm for both perfect and imperfect information games

• URL: http://arxiv.org/abs/2112.03178v2
• Date: Wed, 15 Nov 2023 19:12:12 GMT
• Title: Student of Games: A unified learning algorithm for both perfect and imperfect information games
• Authors: Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, G. Zacharias Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling
• Abstract summary: Student of Games is an algorithm that combines guided search, self-play learning, and game-theoretic reasoning. We prove that Student of Games is sound, converging to perfect play as available computation and approximation capacity increases. Student of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker, and defeats the state-of-the-art agent in Scotland Yard.
• Score: 22.97853623156316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Games have a long history as benchmarks for progress in artificial intelligence. Approaches using search and learning produced strong performance across many perfect information games, and approaches using game-theoretic reasoning and learning demonstrated strong performance for specific imperfect information poker variants. We introduce Student of Games, a general-purpose algorithm that unifies previous approaches, combining guided search, self-play learning, and game-theoretic reasoning. Student of Games achieves strong empirical performance in large perfect and imperfect information games -- an important step towards truly general algorithms for arbitrary environments. We prove that Student of Games is sound, converging to perfect play as available computation and approximation capacity increases. Student of Games reaches strong performance in chess and Go, beats the strongest openly available agent in heads-up no-limit Texas hold'em poker, and defeats the state-of-the-art agent in Scotland Yard, an imperfect information game that illustrates the value of guided search, learning, and game-theoretic reasoning.

Related papers

• Instruction-Driven Game Engine: A Poker Case Study [53.689520884467065]
  The IDGE project aims to democratize game development by enabling a large language model to follow free-form game descriptions and generate game-play processes. We train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs.
  arXiv  Detail & Related papers  (2024-10-17T11:16:27Z)
• Games for Artificial Intelligence Research: A Review and Perspectives [4.44336371847479]
  This paper reviews the games and game-based platforms for artificial intelligence research. It provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques.
  arXiv  Detail & Related papers  (2023-04-26T03:42:31Z)
• The Update-Equivalence Framework for Decision-Time Planning [78.44953498421854]
  We introduce an alternative framework for decision-time planning that is not based on solving subgames, but rather on update equivalence. We derive a provably sound search algorithm for fully cooperative games based on mirror descent and a search algorithm for adversarial games based on magnetic mirror descent.
  arXiv  Detail & Related papers  (2023-04-25T20:28:55Z)
• Learning to Play Stochastic Two-player Perfect-Information Games without Knowledge [5.071342645033634]
  We extend the Descent framework, which enables learning and planning in the context of two-player games with perfect information. We evaluate them on the game Ein wurfelt! against state-of-the-art algorithms. It is our generalization of Descent which obtains the best results.
  arXiv  Detail & Related papers  (2023-02-08T20:27:45Z)
• Revisiting Game Representations: The Hidden Costs of Efficiency in Sequential Decision-making Algorithms [0.6749750044497732]
  Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games. These algorithms traditionally formalize the games using the extensive-form game formalism. We show that a popular workaround involves using a specialized representation based on player specific information-state trees.
  arXiv  Detail & Related papers  (2021-12-20T22:34:19Z)
• ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement Learning [2.7178968279054936]
  We study Gongzhu, a trick-taking game analogous to, but slightly simpler than contract bridge. We train a strong Gongzhu AI ScrofaZero from textittabula rasa by deep reinforcement learning. We introduce new techniques for imperfect information game including stratified sampling, importance weighting, integral over equivalent class, Bayesian inference, etc.
  arXiv  Detail & Related papers  (2021-02-15T12:01:44Z)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)
• Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games [64.11746320061965]
  We study reinforcement learning for text-based games, which are interactive simulations in the context of natural language. We aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.
  arXiv  Detail & Related papers  (2020-10-22T12:40:22Z)
• Learning to Play Sequential Games versus Unknown Opponents [93.8672371143881]
  We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents. Our results include algorithm's regret guarantees that depend on the regularity of the opponent's response.
  arXiv  Detail & Related papers  (2020-07-10T09:33:05Z)
• Navigating the Landscape of Multiplayer Games [20.483315340460127]
  We show how network measures applied to response graphs of large-scale games enable the creation of a landscape of games. We illustrate our findings in domains ranging from canonical games to complex empirical games capturing the performance of trained agents pitted against one another.
  arXiv  Detail & Related papers  (2020-05-04T16:58:17Z)
• 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)

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.