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)
Energy efficiency can often learn much from manufacturing in terms of available analysis techniques, from basic time series analysis through to fuzzy and knowledge based systems and artificial intelligence. On the other hand, manufacturing in many sectors has yet to make use of energy data much beyond finance. Techniques such as complex event processing and(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)
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)