Car Racing

18 papers with code • 0 benchmarks • 0 datasets

Most implemented papers

World Models

hardmaru/WorldModelsExperiments 27 Mar 2018

We explore building generative neural network models of popular reinforcement learning environments.

Simulated Car Racing Championship: Competition Software Manual

ugo-nama-kun/gym_torcs 5 Apr 2013

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 28 Apr 2019

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

HybridRobotics/MPC-CBF 22 Jul 2020

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 20 May 2016

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

fomorians/forward-models 7 Apr 2017

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 8 Apr 2017

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

rperezdattari/Interactive-Learning-with-Corrective-Feedback-for-Policies-based-on-Deep-Neural-Networks 30 Sep 2018

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

google/brain-tokyo-workshop NeurIPS 2019

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

rddy/ReQueST ICML 2020

To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function.