Driving safety is the most important element that needs to be considered for autonomous
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …

To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

Safe exploration is regarded as a key priority area for reinforcement learning research. With
separate reward and safety signals, it is natural to cast it as constrained reinforcement …

Reinforcement machine learning is implemented to survey a series of model potential
energy surfaces and ultimately identify the global minima point. Through sophisticated …

Reinforcement learning (RL) has emerged as a powerful tool for addressing complex
decision making problems in various domains, including aviation. This paper provides a …

Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

Solving optimal control problems serves as the basic demand of industrial control tasks.
Existing methods like model predictive control often suffer from heavy online computational …

Safety is critical to broadening the real-world use of reinforcement learning. Modeling the
safety aspects using a safety-cost signal separate from the reward and bounding the …

The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …