Abstract Model-based reinforcement learning can effectively improve the sample efficiency
of reinforcement learning, but the environment model in this method has errors. The model …

The thriving artificial intelligence (AI) technologies have been used to address various
challenges in the physical world. Currently, AI methods are widely used in perception …

Autonomous Vehicles (AVs) have attracted significant attention in recent years and
Reinforcement Learning (RL) shows remarkable performance in improving the autonomy of …

Learning-based autonomous vehicle trajectory planning methods have shown excellent
performance in a variety of complex traffic scenarios. However, the existing imitation …

This paper introduces a novel algorithm, refer to NEWDQN, which is based on the deep Q-
network (DQN) framework. The primary objective of this algorithm is to optimize the …

This paper presents a multi-objective strategy for trajectory tracking of intelligent electric
vehicles incorporating tracking performance with the energy economy. A model predictive …

Making human-like decisions for autonomous driving in interactive scenarios is crucial and
difficult, requiring the self-driving vehicle to reason about the reactions of interactive vehicles …

The growing adoption of autonomous vehicles (AVs) holds the promise of transforming
transportation systems, enhancing traffic safety, and supporting environmental sustainability …

Autonomous driving does not yet have an industry-standard approach. One of the currently
promising approaches is reinforcement learning. A novel model-based deep reinforcement …

Fruit picking is a process in which a serial-linked robotic arm uses an end-effector for
grasping fruit to minimize human effort, workload, accuracy, and efficiency. The real-world …