Reinforcement learning with safety constraints is promising for autonomous vehicles, of
which various failures may result in disastrous losses. In general, a safe policy is trained by …

Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to
its potential to solve complex classification and control problems. However, existing RL …

Safety is a critical concern when applying reinforcement learning (RL) to real-world control
tasks. However, existing safe RL works either only consider expected safety constraint …

Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …

Reinforcement learning (RL) has been successfully applied to sequential decision-making
problems, eg, playing computer games or solving robotic tasks in simulations. However, RL …

While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …

Reinforcement learning has gradually demonstrated its decision-making ability in
autonomous driving. Reinforcement learning is learning how to map states to actions by …

Path planning for autonomous vehicles in dynamic environments is an important but
challenging problem, due to the constraints of vehicle dynamics and existence of …

Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …

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 …