Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingDeep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
We present TreeIRL, a novel planner for autonomous driving that combines Monte Carlo tree search (MCTS) and inverse reinforcement learning (IRL) to achieve state-of-the-art performance in simulation and in real-world dri…
Reinforcement LearningAutonomous DrivingInforming Autonomous Deception Systems with Cyber Expert Performance Data
The performance of artificial intelligence (AI) algorithms in practice depends on the realism and correctness of the data, models, and feedback (labels or rewards) provided to the algorithm. This paper discusses methods …
reinforcement-learningReinforcement Learning (RL)Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter difficulties when confronted with unexpected …
Anomaly DetectionAutonomous DrivingAutonomous VehiclesModel-based Reinforcement LearningFully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
We study how robots can autonomously learn skills that require a combination of navigation and grasping. While reinforcement learning in principle provides for automated robotic skill learning, in practice reinforcement …
Continual LearningNavigatereinforcement-learningReinforcement Learning+1Exploring reinforcement learning for incident response in autonomous military vehicles
Unmanned vehicles able to conduct advanced operations without human intervention are being developed at a fast pace for many purposes. Not surprisingly, they are also expected to significantly change how military operati…
reinforcement-learningReinforcement Learning