An important challenge for air–ground unmanned systems achieving autonomy is
navigation, which is essential for them to accomplish various tasks in unknown …

Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in
large complex dynamic environments, due to the sparse reward function of Dyna-Q and the …

Proximal Policy Optimization (PPO) is a classical algorithm in reinforcement learning, which
has been tested in a collection of benchmark tasks. In this paper, we test PPO in Unity …

As the extension of cyber–physical systems (CPSs), cyber–physical–social systems
(CPSSs) seamlessly integrate cyber space, physical space, and social space. CPSS provide …

Model-free reinforcement learning algorithms based on entropy regularized have achieved
good performance in control tasks. Those algorithms consider using the entropy-regularized …

In real-world scenarios, making navigation decisions for autonomous driving involves a
sequential set of steps. These judgments are made based on partial observations of the …

Unmanned aerial vehicles (UAVs) have been widely used in military warfare, and realizing
safely autonomous motion control (AMC) in complex unknown environments is a challenge …

Reinforcement learning does not require explicit robot modeling as it learns on its own
based on data, but it has temporal and spatial constraints when transferred to real-world …

Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …

In this work, we propose a Model Predictive Control (MPC)-based Reinforcement Learning
(RL) method for Autonomous Surface Vehicles (ASVs). The objective is to find an optimal …