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Found 26 papers, 11 papers with code
The Update-Equivalence Framework for Decision-Time Planning
Using this framework, 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.
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
Because these regularized equilibria can be made arbitrarily close to Nash equilibria, our result opens the door to a new perspective to solving two-player zero-sum games and yields a simplified framework for decision-time planning in two-player zero-sum games, void of the unappealing properties that plague existing decision-time planning approaches.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
Human-AI Coordination via Human-Regularized Search and Learning
First, we show that our method outperforms experts when playing with a group of diverse human players in ad-hoc teams.
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human play while simultaneously training an agent that responds well to this human model.
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm.
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior.
No-Press Diplomacy from Scratch
Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data.
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker.
A Fine-Tuning Approach to Belief State Modeling
Furthermore, because this specialization occurs after the action or policy has already been decided, BFT does not require the belief model to process it as input.
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
Search is an important tool for computing effective policies in single- and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark fully and partially observable games.
Off-Belief Learning
Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.
Safe Search for Stackelberg Equilibria in Extensive-Form Games
Stackelberg equilibrium is a solution concept in two-player games where the leader has commitment rights over the follower.
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings.
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player zero-sum game.
Unlocking the Potential of Deep Counterfactual Value Networks
Deep counterfactual value networks combined with continual resolving provide a way to conduct depth-limited search in imperfect-information games.
DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies in imperfect-information games with multiple agents.
Improving Policies via Search in Cooperative Partially Observable Games
The first one, single-agent search, effectively converts the problem into a single agent setting by making all but one of the agents play according to the agreed-upon policy.
Stable-Predictive Optimistic Counterfactual Regret Minimization
The CFR framework has been a powerful tool for solving large-scale extensive-form games in practice.
Deep Counterfactual Regret Minimization
This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game.
Solving Imperfect-Information Games via Discounted Regret Minimization
Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to approximately solving large imperfect-information games.
Depth-Limited Solving for Imperfect-Information Games
This paper introduces a principled way to conduct depth-limited solving in imperfect-information games by allowing the opponent to choose among a number of strategies for the remainder of the game at the depth limit.
Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning
Iterative algorithms such as Counterfactual Regret Minimization (CFR) are the most popular way to solve large zero-sum imperfect-information games.
Safe and Nested Subgame Solving for Imperfect-Information Games
Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games.
Reduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning
Counterfactual Regret Minimization (CFR) is the most popular iterative algorithm for solving zero-sum imperfect-information games.
Regret-Based Pruning in Extensive-Form Games
CFR is an iterative algorithm that repeatedly traverses the game tree, updating regrets at each information set. We introduce an improvement to CFR that prunes any path of play in the tree, and its descendants, that has negative regret.
