For robotic vehicles to navigate robustly and safely in unseen environments, it is crucial to
decide the most suitable navigation policy. However, most existing deep reinforcement …

The development of autonomous vehicles has shown great potential to enhance the
efficiency and safety of transportation systems. However, the decision-making issue in …

Solving complex problems using reinforcement learning necessitates breaking down the
problem into manageable tasks, and learning policies to solve these tasks. These policies …

This article addresses the application of deep reinforcement learning (DRL) methods in the
context of local navigation, ie, a robot moves toward a goal location in unknown and …

The proliferation of unmanned aerial vehicles (UAVs) has spawned a variety of intelligent
services, where efficient coordination plays a significant role in increasing the effectiveness …

Deep reinforcement learning, by taking advantage of neural networks, has made great
strides in the continuous control of robots. However, in scenarios where multiple robots are …

This paper provides a study of two deep reinforcement learning techniques for application in
navigation of mobile robots, one of the techniques is the Soft Actor Critic (SAC) that is …

Reinforcement learning (RL) is an effective approach to motion planning in autonomous
driving, where an optimal driving policy can be automatically learned using the interaction …

Reinforcement learning (RL) is effective for autonomous navigation tasks without prior
knowledge of the environment. However, traditional mobile robot navigation algorithms …

The unmanned surface vehicle (USV) has attracted more and more attention because of its
basic ability to perform complex maritime tasks autonomously in constrained environments …