Deep reinforcement learning based mobile robot navigation: A review
K Zhu, T Zhang - Tsinghua Science and Technology, 2021 - ieeexplore.ieee.org
Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement
Learning (DRL) has received significant attention because of its strong representation and …
Learning (DRL) has received significant attention because of its strong representation and …
Deep reinforcement learning for robotics: A survey of real-world successes
Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
Multi-agent deep reinforcement learning for multi-robot applications: A survey
J Orr, A Dutta - Sensors, 2023 - mdpi.com
Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …
example fields in which these successes have taken place include mathematics, games …
Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer
End-to-end approaches are one of the most promising solutions for autonomous vehicles
(AVs) decision-making. However, the deployment of these technologies is usually …
(AVs) decision-making. However, the deployment of these technologies is usually …
Reinforcement learning for robot research: A comprehensive review and open issues
T Zhang, H Mo - International Journal of Advanced Robotic …, 2021 - journals.sagepub.com
Applying the learning mechanism of natural living beings to endow intelligent robots with
humanoid perception and decision-making wisdom becomes an important force to promote …
humanoid perception and decision-making wisdom becomes an important force to promote …
Reinforcement learned distributed multi-robot navigation with reciprocal velocity obstacle shaped rewards
The challenges to solving the collision avoidance problem lie in adaptively choosing optimal
robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a …
robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a …
Deep reinforcement learning of collision-free flocking policies for multiple fixed-wing UAVs using local situation maps
The evolution of artificial intelligence and Internet of Things (IoT) envision a highly integrated
artificial IoT (AIoT) network. Flocking and cooperation with multiple unmanned aerial …
artificial IoT (AIoT) network. Flocking and cooperation with multiple unmanned aerial …
Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach
This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
Drl-vo: Learning to navigate through crowded dynamic scenes using velocity obstacles
This article proposes a novel learning-based control policy with strong generalizability to
new environments that enables a mobile robot to navigate autonomously through spaces …
new environments that enables a mobile robot to navigate autonomously through spaces …
Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments
Robotic navigation in environments shared with other robots or humans remains
challenging because the intentions of the surrounding agents are not directly observable …
challenging because the intentions of the surrounding agents are not directly observable …