To solve the poor performance of reinforcement learning (RL) in the multi-agent flocking
cooperative control under the communication-restricted environments, we propose a multi …

This paper proposes an advanced Reinforcement Learning (RL) method, incorporating
reward-shaping, safety value functions, and a quantum action selection algorithm. The …

Abstract The control of Variable-Speed Wind-Turbines (VSWT) extracting electrical power
from the wind kinetic energy are composed of subsystems that need to be controlled jointly …

For a reinforcement learning (RL) problem without a specified reward function, one may
specify different reward functions to better guide an agent to learn. With different reward …

Policy Gradient methods require many real-world trials. Some of the trials may endanger the
robot system and cause its rapid wear. Therefore, a safe or at least gentle-to-wear …

The aim of this work is to design robust control algorithms of aerial robots, ie quadrotors, for
team transportation of a deformable linear object (DLO). The DLO-robot attachment makes …

We present a cooperative aerial robot system for the transportation of hoses. The hose–
robot attachment makes the whole system physically interconnected but not rigid, so that …

Modular is a powerful and inherently hierarchical concept in the human brain to process a
large variety of complex tasks. Converging evidence has shown several advantages to …

Limited communication resources have gradually become a critical factor toward efficiency
of decentralized large scale multi-agent coordination when both system scales up and tasks …

In this article, a safe deep reinforcement learning (DRL) control method based on a safe
reward shaping method is proposed and applied to the constrained control for an electro …