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Path planning identifies a cost-effective and valid path from an initial point to a target point within an environmental map. Search-based planning methods, which include the well-known A* search, are widely employed in addressing path-planning challenges. These techniques have found application in various domains, inc... | Path Planning using Neural A* Search
Ryo Yonetani * 1 Tatsunori Taniai * 1 Mohammadamin Barekatain 1 2 Mai Nishimura 1 Asako Kanezaki 3
Abstract
We present Neural A*, a novel data-driven search
method for path planning problems. Despite the
recent increasing attention to data-driven path
planning, machine learning appr... |
In image recognition, researchers and developers constantly seek innovative approaches to enhance the accuracy and efficiency of computer vision systems. Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and ... | 1 INTRODUCTION Self-attention-based architectures, in particular Transformers (Vaswani et al., 2017), have become the model of choice in natural language processing (NLP). The dominant approach is to pre-train on a large text corpus and then fine-tune on a smaller task-specific dataset (Devlin et al., 2019). Thanks to Tr... |
With the constant advancements in technology, Artificial Intelligence is successfully enabling computers to think and learn in a manner comparable to that of humans by imitating human brainpower. Recent advances in Artificial intelligence, Machine Learning (ML), and Deep Learning have helped improve multiple fields, in... | Vector Quantized Models for Planning
Sherjil Ozair * 1 2 Yazhe Li * 1 Ali Razavi 1 Ioannis Antonoglou 1 Aäron van den Oord 1 Oriol Vinyals 1
Abstract
Recent developments in the field of model-based
RL have proven successful in a range of envi-
ronments, especially ones where planning is es-
sential. However, such succes... |
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