Efficient reinforcement learning for autonomous driving with parameterized skills and priors
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 …
diverse driving situations. Many manually designed driving policies are difficult to scale to …
Boosting offline reinforcement learning for autonomous driving with hierarchical latent skills
Learning-based vehicle planning is receiving increasing attention with the emergence of
diverse driving simulators and large-scale driving datasets. While offline reinforcement …
diverse driving simulators and large-scale driving datasets. While offline reinforcement …
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
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 …
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …
Drivergym: Democratising reinforcement learning for autonomous driving
Despite promising progress in reinforcement learning (RL), developing algorithms for
autonomous driving (AD) remains challenging: one of the critical issues being the absence …
autonomous driving (AD) remains challenging: one of the critical issues being the absence …
Fastrlap: A system for learning high-speed driving via deep rl and autonomous practicing
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 …
from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains …
Improved deep reinforcement learning with expert demonstrations for urban autonomous driving
Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …
Efficient deep reinforcement learning with imitative expert priors for autonomous driving
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 …
driving. However, the low sample efficiency and difficulty of designing reward functions for …
End-to-end model-free reinforcement learning for urban driving using implicit affordances
M Toromanoff, E Wirbel… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
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 …
experiments and not rule-based control methods. However, there is no RL algorithm yet …
Offline reinforcement learning for autonomous driving with real world driving data
Since traditional reinforcement learning (RL) approaches need active online interaction with
the environment, previous works are mainly investigated in the simulation environment …
the environment, previous works are mainly investigated in the simulation environment …
A reinforcement learning benchmark for autonomous driving in general urban scenarios
Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …
decision and control in autonomous driving. However, current approaches have yet to …