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Found 10 papers, 2 papers with code
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories
Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning.
NeurIPS 2022 Competition: Driving SMARTS
The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS.
Benchmarking Constraint Inference in Inverse Reinforcement Learning
When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints.
Learning Soft Constraints From Constrained Expert Demonstrations
We consider the setting where the reward function is given, and the constraints are unknown, and propose a method that is able to recover these constraints satisfactorily from the expert data.
Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning
The approach is evaluated against two challenging scenarios of pedestrians crossing with occlusion and curved roads with a limited field of view.
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning
Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety.
How To Not Drive: Learning Driving Constraints from Demonstration
We propose a new scheme to learn motion planning constraints from human driving trajectories.
CoachNet: An Adversarial Sampling Approach for Reinforcement Learning
Despite the recent successes of reinforcement learning in games and robotics, it is yet to become broadly practical.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
Perception as prediction using general value functions in autonomous driving applications
We demonstrate perception as prediction by learning to predict an agent's front safety and rear safety with GVFs, which encapsulate anticipation of the behavior of the vehicle in front and in the rear, respectively.
