A Simple End-to-end Deep Reinforcement Learning Method for Situation Calculus


A Simple End-to-end Deep Reinforcement Learning Method for Situation Calculus – We propose a novel neural model for action recognition and action planning, in which information is encoded explicitly by the task-oriented context of the scene. The structure of the context is modeled by a pair of temporal- and spatial-dependent neural networks. As the task-oriented context of the scene plays a key role in the learned performance, this model achieves state-of-the-art results in both datasets.

In this paper, we propose a general framework to learn an objective function for action recognition and planning using convolutional neural networks. We show how to extract features of the objective function in the training set of such networks. We also show that the learning of feature maps of the objective function is a crucial step for a successful decision making problem. In doing so, we show how to make use of the available information in a supervised learning setting to learn a discriminative objective function. Our training data are shown to be rich in semantic information and we show how to use state-of-the-art image-level classification techniques to further improve the learning performance.

Automatically identifying keypoints is a challenging problem in data mining. Recent work has shown that finding the most important keypoints is a multi-step problem, which has led to many successful research efforts. However, keypoints are commonly not detected automatically after an important step in a system architecture. This paper presents an algorithm for identifying keypoints with high probability. The algorithm is an extension of the traditional one-step learning algorithms. It aims to find keypoints that lie in a global proximity in data. We present an algorithm for the identification of keypoints with high probability. We first show how to identify keypoints that lie in a global proximity using a sparse metric, and then propose a variant of the algorithm that can be used to find keypoint locations. The algorithm is evaluated on the problem of finding the most important keypoints by means of a simple and fast algorithm. Experimental results show that our algorithm outperforms other related keypoint mining algorithms.

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A Simple End-to-end Deep Reinforcement Learning Method for Situation Calculus

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  • On the convergence of the gradient of the Hessian

    Video Highlights and Video Statistics in First PlaceAutomatically identifying keypoints is a challenging problem in data mining. Recent work has shown that finding the most important keypoints is a multi-step problem, which has led to many successful research efforts. However, keypoints are commonly not detected automatically after an important step in a system architecture. This paper presents an algorithm for identifying keypoints with high probability. The algorithm is an extension of the traditional one-step learning algorithms. It aims to find keypoints that lie in a global proximity in data. We present an algorithm for the identification of keypoints with high probability. We first show how to identify keypoints that lie in a global proximity using a sparse metric, and then propose a variant of the algorithm that can be used to find keypoint locations. The algorithm is evaluated on the problem of finding the most important keypoints by means of a simple and fast algorithm. Experimental results show that our algorithm outperforms other related keypoint mining algorithms.


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