Deep Reinforcement Learning for Goal-Directed Exploration in Sequential Decision Making Scenarios


Deep Reinforcement Learning for Goal-Directed Exploration in Sequential Decision Making Scenarios – In this paper, we propose a novel Deep Reinforcement Learning system, Neural-SteerNet, which can be regarded as a general reinforcement learning system. This system has been tested on a dataset of real-world tasks as well as on a set of tasks with few rewards. We show that the Neural-SteerNet can learn to navigate successfully from a relatively low level problem. Moreover, the network can successfully learn to find the target objects of the task and can navigate, and perform well within the visual environment. Experiments conducted on both real and simulated data illustrate that the Neural-SteerNet can perform better than other reinforcement learning systems on the task and can reach higher accuracies.

This paper studies the effect of two different types of information: (1) context and (2) message. Given a set of data, a text is associated with the context of that text, and the message is the message represented by texts. In this paper, we apply Convolutional Neural Networks (CNNs) for a simple supervised retrieval problem. The objective is to learn a compact set of convolutional networks for this task. We construct several different compact CNN architectures from the existing methods: the proposed architectures are based on convolutional neural networks (CNNs) and use multiple CNNs to handle all the features for the input data. We evaluate these CNN architectures on the task of answering Question A regarding the topic of the question. Experimental results demonstrate that the new architectures are more well suited in terms of the retrieval task.

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Deep Reinforcement Learning for Goal-Directed Exploration in Sequential Decision Making Scenarios

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  • A Manchure Library for the Semantic Image Tagging of Images

    Dependency-Based Deep Recurrent Models for Answer RecommendationThis paper studies the effect of two different types of information: (1) context and (2) message. Given a set of data, a text is associated with the context of that text, and the message is the message represented by texts. In this paper, we apply Convolutional Neural Networks (CNNs) for a simple supervised retrieval problem. The objective is to learn a compact set of convolutional networks for this task. We construct several different compact CNN architectures from the existing methods: the proposed architectures are based on convolutional neural networks (CNNs) and use multiple CNNs to handle all the features for the input data. We evaluate these CNN architectures on the task of answering Question A regarding the topic of the question. Experimental results demonstrate that the new architectures are more well suited in terms of the retrieval task.


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