Car Racing
19 papers with code • 0 benchmarks • 0 datasets
https://gym.openai.com/envs/CarRacing-v0/
Benchmarks
These leaderboards are used to track progress in Car Racing
Latest papers with no code
RaceLens: A Machine Intelligence-Based Application for Racing Photo Analysis
This paper presents RaceLens, a novel application utilizing advanced deep learning and computer vision models for comprehensive analysis of racing photos.
End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing
Reinforcement Learning (RL) has emerged as a transformative approach in the domains of automation and robotics, offering powerful solutions to complex problems that conventional methods struggle to address.
DADAgger: Disagreement-Augmented Dataset Aggregation
DAgger is an imitation algorithm that aggregates its original datasets by querying the expert on all samples encountered during training.
CT-DQN: Control-Tutored Deep Reinforcement Learning
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy.
Event Tables for Efficient Experience Replay
Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems.
Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning
Through the method of Learning Feedback Linearization, we seek to learn a linearizing controller to simplify the process of controlling a car to race autonomously.
Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs.
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning
Professional race-car drivers can execute extreme overtaking maneuvers.
Sim-To-Real Transfer for Miniature Autonomous Car Racing
With our method, a model with 18. 4\% completion rate on the testing track is able to help teach a student model with 52\% completion.