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
Most implemented papers
World Models
hardmaru/WorldModelsExperiments
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We explore building generative neural network models of popular reinforcement learning environments.
Simulated Car Racing Championship: Competition Software Manual
This manual describes the competition software for the Simulated Car Racing Championship, an international competition held at major conferences in the field of Evolutionary Computation and in the field of Computational Intelligence and Games.
Deep Neuroevolution of Recurrent and Discrete World Models
sebastianrisi/ga-world-models
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Instead of the relatively simple architectures employed in most RL experiments, world models rely on multiple different neural components that are responsible for visual information processing, memory, and decision-making.
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control.
Query-Efficient Imitation Learning for End-to-End Autonomous Driving
mbhenaff/EEN
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A policy function trained in this way however is known to suffer from unexpected behaviours due to the mismatch between the states reachable by the reference policy and trained policy functions.
Recurrent Environment Simulators
Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently.
Deep Reinforcement Learning framework for Autonomous Driving
gowriaddepalli/ML_DL_Research_collab_base
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This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks.
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).
Weight Agnostic Neural Networks
We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training.
Learning Human Objectives by Evaluating Hypothetical Behavior
To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function.
