The Role of Intensive Regression in Learning to Play StarCraft


The Role of Intensive Regression in Learning to Play StarCraft – In this paper we present a novel framework for predicting the importance of an actor’s performance in StarCraft games using a sequence of simple examples. This framework applies probabilistically, learning to a player’s state in a game, and to a character’s actions in the game via the model of the actor’s performance on a sequence of simple examples. We show that this framework outperforms the state-of-the-art predictions and we explore the idea to use probabilistic models through different learning methods. We show that learning to perform at the level of a human actor results in significant improvements over classical probabilistic models that do not learn to play at this level of a human actor.

We perform an open, open-domain test of how the proposed approach compares to a wide range of existing methods. Our goal is to show that the proposed approaches tend to deliver the desired outcome in a low-resource setting. In our test, we present an algorithm for comparing two different tracking and tracking approaches. The algorithms are based on a simple iterative model of two images where the goal is to find the best one. We also provide experiments with two different approaches: a low-resource and a large-resource tracking approach in an open-domain setting. Results on several real-world databases show the superiority of the proposed approaches in terms of accuracy, recall and retrieval.

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The Role of Intensive Regression in Learning to Play StarCraft

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    A New Approach to Online Multi-Camera Tracking and TrackingWe perform an open, open-domain test of how the proposed approach compares to a wide range of existing methods. Our goal is to show that the proposed approaches tend to deliver the desired outcome in a low-resource setting. In our test, we present an algorithm for comparing two different tracking and tracking approaches. The algorithms are based on a simple iterative model of two images where the goal is to find the best one. We also provide experiments with two different approaches: a low-resource and a large-resource tracking approach in an open-domain setting. Results on several real-world databases show the superiority of the proposed approaches in terms of accuracy, recall and retrieval.


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