Path planning based on deep reinforcement learning for autonomous underwater vehicles under ocean current disturbance
The path planning issue of the underactuated autonomous underwater vehicle (AUV) under
ocean current disturbance is studied in this paper. In order to improve the AUV's path …
ocean current disturbance is studied in this paper. In order to improve the AUV's path …
Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
A brief survey: Deep reinforcement learning in mobile robot navigation
Conventional navigation techniques have mainly relied on a global information approach,
wherein pre-built laser or camera environment maps are used to construct a path from a …
wherein pre-built laser or camera environment maps are used to construct a path from a …
Robot skill learning and the data dilemma it faces: a systematic review
Purpose Compared with traditional methods relying on manual teaching or system
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …
Deep reinforcement learning for the management of software-defined networks in smart farming
RS Alonso, I Sittón-Candanedo… - … Conference on Omni …, 2020 - ieeexplore.ieee.org
The Internet of Things and the millions of devices that generate and collect data through
sensors to send it to the Cloud are part of the life of users in many contexts, including smart …
sensors to send it to the Cloud are part of the life of users in many contexts, including smart …
iTD3-CLN: Learn to navigate in dynamic scene through Deep Reinforcement Learning
This paper proposes iTD3-CLN, a Deep Reinforcement Learning (DRL) based low-level
motion controller, to achieve map-less autonomous navigation in dynamic scene. We …
motion controller, to achieve map-less autonomous navigation in dynamic scene. We …
Navigation of mobile robots based on deep reinforcement learning: Reward function optimization and knowledge transfer
This paper presents an end-to-end online learning navigation method based on deep
reinforcement learning (DRL) for mobile robots, whose objective is that mobile robots can …
reinforcement learning (DRL) for mobile robots, whose objective is that mobile robots can …
Learn to navigate autonomously through deep reinforcement learning
In this article, we propose a deep reinforcement learning (DRL) algorithm as well as a novel
tailored neural network architecture for mobile robots to learn navigation policies …
tailored neural network architecture for mobile robots to learn navigation policies …
Achieving real-time path planning in unknown environments through deep neural networks
Real-time path planning is crucial for intelligent vehicles to achieve autonomous navigation.
In this paper, we propose a novel deep neural network (DNN) based method for real-time …
In this paper, we propose a novel deep neural network (DNN) based method for real-time …
Multimodal deep reinforcement learning with auxiliary task for obstacle avoidance of indoor mobile robot
It is an essential capability of indoor mobile robots to avoid various kinds of obstacles.
Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great …
Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great …