When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

Learning-based vehicle planning is receiving increasing attention with the emergence of
diverse driving simulators and large-scale driving datasets. While offline reinforcement …

Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …

Despite promising progress in reinforcement learning (RL), developing algorithms for
autonomous driving (AD) remains challenging: one of the critical issues being the absence …

We present a system that enables an autonomous small-scale RC car to drive aggressively
from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own
experiments and not rule-based control methods. However, there is no RL algorithm yet …

Since traditional reinforcement learning (RL) approaches need active online interaction with
the environment, previous works are mainly investigated in the simulation environment …

Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …