Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv:2403.18209v1 Announce Type: new
Abstract: Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but they still permit unsafe state occurrence, which is unacceptable in autonomous driving tasks. Moreover, …

abstract agent applications arxiv autonomous autonomous driving constraints cs.ai cs.lg cs.ro decision driving environment industrial making process reinforcement reinforcement learning requirements safety tasks the environment training type