Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Mobile robots contributed significantly to the intelligent development of human society, and
the motion-planning policy is critical for mobile robots. This paper reviews the methods …

Learning to adapt to a series of different goals in visual navigation is challenging. In this
work, we present a model-embedded actor–critic architecture for the multigoal visual …

Sustainable manufacturing practices are crucial in job shop scheduling (JSS) to enhance
the resilience of production systems against resource shortages and regulatory changes …

We present a novel approach to improve the performance of deep reinforcement learning
(DRL) based outdoor robot navigation systems. Most, existing DRL methods are based on …