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04/25/2023
The Update Equivalence Framework for Decision-Time Planning
The process of revising (or constructing) a policy immediately prior to ...
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01/22/2023
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
In their seminal work, Nayyar et al. (2013) showed that imperfect inform...
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10/11/2022
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
No-press Diplomacy is a complex strategy game involving both cooperation...
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10/11/2022
Human-AI Coordination via Human-Regularized Search and Learning
We consider the problem of making AI agents that collaborate well with h...
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06/12/2022
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Algorithms designed for single-agent reinforcement learning (RL) general...
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04/11/2022
Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization
We extend the classic regret minimization framework for approximating eq...
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12/14/2021
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
We consider the task of building strong but human-like policies in multi...
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10/06/2021
No-Press Diplomacy from Scratch
Prior AI successes in complex games have largely focused on settings wit...
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09/30/2021
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Lookahead search has been a critical component of recent AI successes, s...
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06/16/2021
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
Search is an important tool for computing effective policies in single- ...
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03/06/2021
Off-Belief Learning
The standard problem setting in Dec-POMDPs is self-play, where the goal ...
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02/02/2021
Safe Search for Stackelberg Equilibria in Extensive-Form Games
Stackelberg equilibrium is a solution concept in two-player games where ...
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10/06/2020
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Prior AI breakthroughs in complex games have focused on either the purel...
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07/27/2020
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
The combination of deep reinforcement learning and search at both traini...
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07/20/2020
Unlocking the Potential of Deep Counterfactual Value Networks
Deep counterfactual value networks combined with continual resolving pro...
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06/18/2020
DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
We introduce DREAM, a deep reinforcement learning algorithm that finds o...
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12/05/2019
Improving Policies via Search in Cooperative Partially Observable Games
Recent superhuman results in games have largely been achieved in a varie...
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02/13/2019
Stable-Predictive Optimistic Counterfactual Regret Minimization
The CFR framework has been a powerful tool for solving large-scale exten...
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11/01/2018
Deep Counterfactual Regret Minimization
Counterfactual Regret Minimization (CFR) is the leading algorithm for so...
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09/11/2018
Solving Imperfect-Information Games via Discounted Regret Minimization
Counterfactual regret minimization (CFR) is a family of iterative algori...
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05/21/2018
Depth-Limited Solving for Imperfect-Information Games
A fundamental challenge in imperfect-information games is that states do...
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05/08/2017
Safe and Nested Subgame Solving for Imperfect-Information Games
In imperfect-information games, the optimal strategy in a subgame may de...
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09/12/2016
