Learn to Navigate: Cooperative Path Planning for Unmanned Surface Vehicles Using Deep Reinforcement Learning
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TLDR
This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments.Citations
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Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey
TL;DR: The rapid development of artificial intelligence significantly promotes collision avoidance navigation of maritime autonomous surface ships (MASS), which in turn provides prominent services in maritime environments and enlarges the opportunity for coordinated and interconnected operations.
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Path Planning of Coastal Ships Based on Optimized DQN Reward Function
TL;DR: A coastal ship path planning model based on the optimized deep Q network (DQN) algorithm that can plan the optimal path in line with the actual navigation rules, and improve the safety, economy and autonomous decision-making ability of ship navigation is proposed.
Journal ArticleDOI
A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case
TL;DR: In this article, a centralized Convolutional Deep Q-network was proposed for multi-agent patrolling in a case-study scenario, where a tailored reward function was created which penalizes illegal actions and rewards visiting idle cells.
Journal ArticleDOI
A survey on deep reinforcement learning architectures, applications and emerging trends
Surjeet Balhara,Nishu Gupta,Ahmed Alkhayyat,Isha Bharti,Rami Qays Malik,Sarmad Nozad Mahmood,Firas Abedi +6 more
TL;DR: Deep Reinforcement Learning (DRL) as discussed by the authors is one of the most popular reinforcement learning algorithms for handling dynamic environments without any explicit programming and it has grasped great attention in the areas of natural language processing, speech recognition, computer vision and image classification.
Journal ArticleDOI
Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review
TL;DR: In this paper, the authors reviewed the methods based on motion-planning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment, and the conventional methods of DRL are categorized to value-based, policy-based and actor-critic-based algorithms.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Posted Content
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Alex Graves,Ioannis Antonoglou,Daan Wierstra,Martin Riedmiller +6 more
TL;DR: This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
Journal ArticleDOI
Mastering the game of Go without human knowledge
David Silver,Julian Schrittwieser,Karen Simonyan,Ioannis Antonoglou,Aja Huang,Arthur Guez,Thomas Hubert,Lucas Baker,Matthew Lai,Adrian Bolton,Yutian Chen,Timothy P. Lillicrap,Fan Hui,Laurent Sifre,George van den Driessche,Thore Graepel,Demis Hassabis +16 more
TL;DR: An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.