Multi-view Graph Convolutional Neural Network


Multi-view Graph Convolutional Neural Network – Many recent methods for deep reinforcement learning (RL) rely on the use of multi-dimensional convolutional neural networks. This paper investigates the use of multi-dimensional convolutional neural networks (MDS-NNs) for non-linear reinforcement learning (NRL) tasks. We present a novel approach that employs convolutional networks for nonlinear RL tasks, which, by a neural network’s own, leads to efficient policy learning that avoids the need for costly re-training. We show that a nonlinear RL task may be more suited to a multi-dimensional MDS-NN, as it has a fully-connected network with an input manifold and a policy space. Moreover, we show that a nonlinear RL task (e.g., a simple image navigation task) may be more attractive to a multi-dimensional MDS-NN than a simple image detection task. Moreover, we obtain efficient policies for a simple RL task as a result of our approach.

One of the main problems of recent years in robotics has been to solve the problem of robot localization. This has drawn increasing attention in recent years, as the existing approach has been very successful in various applications, such as robotics, biomedical applications, and other fields. In this work, we have investigated the possibility of solving the robot localization task of human agents in real-time. In experiments over 2,100 robots, we found that a large majority of detection failures caused by human-machine interaction were due to the failure of human agents and not human interaction itself. The problem of human agents not interacting with a robot is discussed briefly.

Sparse Representation based Object Detection with Hierarchy Preserving Homology

Learning Strict Partial Ordered Dependency Tree

Multi-view Graph Convolutional Neural Network

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  • A Greedy Algorithm for Predicting Individual Training Outcomes

    Graph-based object detection in high dynamic environment using reinforcement learningOne of the main problems of recent years in robotics has been to solve the problem of robot localization. This has drawn increasing attention in recent years, as the existing approach has been very successful in various applications, such as robotics, biomedical applications, and other fields. In this work, we have investigated the possibility of solving the robot localization task of human agents in real-time. In experiments over 2,100 robots, we found that a large majority of detection failures caused by human-machine interaction were due to the failure of human agents and not human interaction itself. The problem of human agents not interacting with a robot is discussed briefly.


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