End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that demand dense on-policy supervision. On the contrary, automated experts that leverage privileged information can efficiently generate large scale on-policy and off-policy demonstrations. However, existing automated experts for urban driving make heavy use of hand-crafted rules and perform suboptimally even on driving simulators, where ground-truth information is available. To address these issues, we train a reinforcement learning expert that maps bird's-eye view images to continuous low-level actions. While setting a new performance upper-bound on CARLA, our expert is also a better coach that provides informative supervision signals for imitation learning agents to learn from. Supervised by our reinforcement learning coach, a baseline end-to-end agent with monocular camera-input achieves expert-level performance. Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense benchmark and state-of-the-art performance on the challenging public routes of the CARLA LeaderBoard.
Code (3)
Tasks
Autonomous DrivingImitation Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning to Drive Using Sparse Imitation Reinforcement Learning
In this paper, we propose Sparse Imitation Reinforcement Learning (SIRL), a hybrid end-to-end control policy that combines the sparse expert driving knowledge with reinforcement learning (RL) policy for autonomous drivin…
Autonomous Drivingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision
Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task predictio…
Autonomous DrivingMulti-Task LearningGenerative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments
Autonomous driving is a complex task, which has been tackled since the first self-driving car ALVINN in 1989, with a supervised learning approach, or behavioral cloning (BC). In BC, a neural network is trained with state…
Autonomous DrivingAutonomous NavigationImitation LearningReinforcement Learning (RL)SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching
High-quality curated datasets are essential for training and evaluating AI approaches, but are often lacking in embodied interactive domains where language and physical action are intertwined. In particular, few datasets…
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches for autonom…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2