Journal ArticleDOI
Deep reinforcement learning-based collision avoidance for an autonomous ship
TLDR
A collision avoidance method that quantitatively assesses the collision risk and then generates an avoidance path that reliably avoided collisions through flexible paths for complex and unexpected changes in situations compared to the A* algorithm.About:
This article is published in Ocean Engineering.The article was published on 2021-08-15. It has received 43 citations till now. The article focuses on the topics: Collision avoidance & Motion planning.read more
Citations
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Journal ArticleDOI
Deep Reinforcement Learning-Based Path Control and Optimization for Unmanned Ships
TL;DR: This paper uses deep reinforcement learning to solve the optimization problem in the path planning and management of unmanned ships, and takes the waiting time at the corner of the path as the optimization goal to minimize the total travel time of unmanned ship passing through the path.
Journal ArticleDOI
MASS autonomous navigation system based on AIS big data with dueling deep Q networks prioritized replay reinforcement learning
TL;DR: Wang et al. as mentioned in this paper proposed a MASS autonomous navigation system using dueling deep Q networks prioritized replay (Dueling-DQNPR) based on the ship automatic identification system (AIS) big data.
Journal ArticleDOI
Ship route planning in Arctic Ocean based on POLARIS
TL;DR: In this article, a ship route planning system comprising a performance evaluation model and an optimization model is proposed to identify an economical and safe shipping route in the Arctic Ocean, a risk assessment of the ice regime and optimization of the route that minimizes the total power consumption should be performed.
Journal ArticleDOI
A Novel Reinforcement Learning Collision Avoidance Algorithm for USVs Based on Maneuvering Characteristics and COLREGs
TL;DR: A reinforcement learning collision avoidance (RLCA) algorithm is proposed that complies with USV maneuverability that bridged the divide between USV navigation status information and collision avoidance behavior, resulting in successfully planning a safe and economical path to the terminal.
Journal ArticleDOI
Global path planning algorithm based on double DQN for multi-tasks amphibious unmanned surface vehicle
TL;DR: In this article , a global path planning algorithm based on double deep Q networks (DDQN) is proposed for amphibious unmanned surface vehicles (USVs) to make path planning for an amphibious UAV.
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.
Posted Content
Proximal Policy Optimization Algorithms
TL;DR: A new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent, are proposed.
Proceedings Article
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
TL;DR: This paper proposes soft actor-critic, an off-policy actor-Critic deep RL algorithm based on the maximum entropy reinforcement learning framework, and achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off- policy methods.
Proceedings ArticleDOI
Target-driven visual navigation in indoor scenes using deep reinforcement learning
TL;DR: This article proposed an actor-critic model whose policy is a function of the goal as well as the current state, which allows better generalization and generalizes across targets and scenes.
Proceedings Article
Off-Policy Deep Reinforcement Learning without Exploration
TL;DR: This paper introduces a novel class of off-policy algorithms, batch-constrained reinforcement learning, which restricts the action space in order to force the agent towards behaving close to on-policy with respect to a subset of the given data.