Demystifying Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making

arXiv:2403.11432v1 Announce Type: cross
Abstract: With the advent of universal function approximators in the domain of reinforcement learning, the number of practical applications leveraging deep reinforcement learning (DRL) has exploded. Decision-making in automated driving tasks has emerged as a chief application among them, taking the sensor data or the higher-order kinematic variables as the input and providing a discrete choice or continuous control output. However, the black-box nature of the models presents an overwhelming limitation that restricts the real-world deployment …

abstract application applications arxiv automated autonomous autonomous vehicle cs.ai cs.lg cs.ro data decision domain driving function making practical reinforcement reinforcement learning sensor tasks them type universal variables