Noam Brown

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The leading approach for solving large imperfect-information games is automated abstraction followed by running an equilibrium finding algorithm. We introduce a distributed version of the most commonly used equilibrium-finding algorithm , counterfactual regret minimization (CFR), which enables CFR to scale to dramatically larger abstractions and numbers of(More)
Regret matching is a widely-used algorithm for learning how to act. We begin by proving that regrets on actions in one setting (game) can be transferred to warm start the regrets for solving a different setting with same structure but different payoffs that can be written as a function of parameters. We prove how this can be done by carefully discounting(More)
Building occupancy sensing is useful for control of building services such as lighting and ventilation, enabling energy savings, whilst maintaining a comfortable environment. However, a precise and reliable measurement of occupancy still remains difficult. Existing technologies are plagued with a number of issues ranging from unreliable data, maintaining(More)
Unlike perfect-information games, imperfect-information games cannot be solved by decomposing the game into subgames that are solved independently. Thus all decisions must consider the strategy of the game as a whole. While it is not possible to solve an imperfect-information game exactly through decomposition, it is possible to approximate solutions, or(More)
Counterfactual Regret Minimization (CFR) is the most popular iterative algorithm for solving zero-sum imperfect-information games. Regret-Based Pruning (RBP) is an improvement that allows poorly-performing actions to be temporarily pruned, thus speeding up CFR. We introduce Total RBP, a new form of RBP that reduces the space requirements of CFR as actions(More)
Imperfect-information games, where players have private information, pose a unique challenge in artificial intelligence. In recent years, Heads-Up No-Limit Texas Hold'em poker, a popular version of poker, has emerged as the primary benchmark for evaluating game-solving algorithms for imperfect-information games. We demonstrate a winning agent from the 2016(More)
Regret minimization is widely used in determining strategies for imperfect-information games and in online learning. In large games, computing the regrets associated with a single iteration can be slow. For this reason, pruning – in which parts of the decision tree are not traversed in every iteration – has emerged as an essential method for speeding up(More)