Car Racing
20 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
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning
Autonomous car racing is a major challenge in robotics.
A Probabilistic Framework for Imitating Human Race Driver Behavior
To approach this problem, we propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules.
Challenging On Car Racing Problem from OpenAI gym
This project challenges the car racing problem from OpenAI gym environment.
Unsupervised-Learning of time-varying features
We present an architecture based on the conditional Variational Autoencoder to learn a representation of transformations in time-sequence data.
Robust Optimization through Neuroevolution
We propose a method for evolving solutions that are robust with respect to variations of the environmental conditions (i. e. that can operate effectively in new conditions immediately, without the need to adapt to variations).
Choosing to Rank
Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict.
Programmatically Interpretable Reinforcement Learning
Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language.
Learning to Race through Coordinate Descent Bayesian Optimisation
On the other hand, the cost to evaluate the policy's performance might also be high, being desirable that a solution can be found with as few interactions as possible with the real system.
Autonomous Driving in Reality with Reinforcement Learning and Image Translation
Supervised learning is widely used in training autonomous driving vehicle.
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it.
