Hierarchical Image Object Search Based on Deep Reinforcement Learning [PDF]
International Journal of Advanced Network, Monitoring and Controls, 2020 AbstractObject detection technology occupies a pivotal position in the field of modern computer vision research, its purpose is to accurately locate the object human beings are looking for in the image and classify the object. With the development of deep learning technology, convolutional neural networks are widely used because of their outstanding ...
Zhang, Wei, Yao, Hongge, Tan, Yuxing
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Deep Reinforcement Learning. Studiu de caz: Deep Q-Network [PDF]
Revista Română de Informatică și Automatică, 2019 Artificial Intelligence (AI) became today perhaps the most up-to-date topic in many areas. One of the main goals of AI is to create completely autonomous agents able to interact with the surrounding world and learn by trial and error optimal behaviors ...
Mihnea Horia VREJOIU
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Vehicle Intelligent Control Method Based on Deep Reinforcement Learning PPO [PDF]
Jisuanji gongchengThis study proposes a Proximal Policy Optimization (PPO)-based vehicle intelligence control method to improve vehicle driving efficiency in a mixed environment on highways and reduce traffic accidents.
YE Baolin, WANG Xin, LI Lingxi, WU Weimin
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Hierarchical Object Detection with Deep Reinforcement Learning
, 2017 This work introduces a model for Hierarchical Object Detection with Deep Reinforcement Learning (HOD-DRL). The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention on five different predefined ...
Bellver, Míriam +3 more
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Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning [PDF]
, 2017 This paper presents a hierarchical framework based on deep reinforcement learning that learns a diversity of policies for humanoid balance control. Conventional zero moment point based controllers perform limited actions during under-actuation, whereas ...
Komura, Taku, Li, Zhibin, Yang, Chuanyu
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CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms [PDF]
, 2019 How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Guo, Zilong +11 more
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A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control
Remote Sensing, 2023 For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method.
Rong Zhou +3 more
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Hierarchical Reinforcement Learning with Deep Nested Agents
, 2018 11 ...
Brittain, Marc, Wei, Peng
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Extensible Hierarchical Multi-Agent Reinforcement-Learning Algorithm in Traffic Signal Control
Applied Sciences, 2022 Reinforcement-learning (RL) algorithms have made great achievements in many scenarios. However, in large-scale traffic signal control (TSC) scenarios, RL still falls into local optima when controlling multiple signal lights.
Pengqian Zhao, Yuyu Yuan, Ting Guo
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TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
, 2021 Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in practical scenarios consisting of abrupt dynamics. This allows one to rethink the problem setup in light of practical challenges.
Suri, Karush +3 more
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