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 …

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement
Learning (DRL) has received significant attention because of its strong representation and …

Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

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 …

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 …

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 …

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 …

Motion planning is critical to realize the autonomous operation of mobile robots. As the
complexity and randomness of robot application scenarios increase, the planning capability …

As unmanned aerial vehicles (UAVs) play an increasingly significant role in modern society,
using reinforcement learning to build safe multi-UAV navigation algorithms has become a …

This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …