From the control theory, the best control gain produces a balance between the trajectory
tracking accuracy and control energy consumption. The random search of the bat algorithm …

Motion planning and its optimization is vital and difficult for redundant robot manipulator in
an environment with obstacles. In this article, a general motion planning framework that …

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 …

It is an interesting open problem to enable robots to efficiently and effectively learn long-
horizon manipulation skills. Motivated to augment robot learning via more effective …

Deep deterministic policy gradient (DDPG) is a powerful reinforcement learning algorithm for
large-scale continuous controls. DDPG runs the back-propagation from the state-action …

Object packing by autonomous robots is an important challenge in warehouses and logistics
industry. Most conventional data-driven packing planning approaches focus on regular …

In this article, we propose an algorithm that combines actor–critic-based off-policy method
with consensus-based distributed training to deal with multiagent deep reinforcement …

Learning with sparse rewards remains a challenging problem in reinforcement learning
(RL). In particular, for sequential object manipulation tasks, the RL agent generally only …

Though reinforcement learning (RL) has shown an outstanding capability for solving
complex computational problems, most RL algorithms lack an explicit method that would …

This paper proposes a novel hierarchical reinforcement learning (HRL) framework of
complex manipulation tasks which integrates the human prior knowledge. The framework …