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
Rank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous factors exists.
Pragmatic Image Compression for Human-in-the-Loop Decision-Making
Standard lossy image compression algorithms aim to preserve an image's appearance, while minimizing the number of bits needed to transmit it.
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.
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
ntavanhoeffelen/daif_carracing
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Despite the potential of active inference for visual-based control, learning the model and the preferences (priors) while interacting with the environment is challenging.
An Unsupervised Video Game Playstyle Metric via State Discretization
DSobscure/cgi_drl_platform
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In this paper, we propose the first metric for video game playstyles directly from the game observations and actions, without any prior specification on the playstyle in the target game.
MicroRacer: a didactic environment for Deep Reinforcement Learning
MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning.
A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria
Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous prediction and control in such scenarios.
Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
vroulet/ilqc
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We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint.
Decision-making and control with diffractive optical networks
qiujumin/don-rl
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Our work represents a solid step forward in advancing diffractive optical networks, which promises a fundamental shift from the target-driven control of a pre-designed state for simple recognition or classification tasks to the high-level sensory capability of artificial intelligence.
Stabilizing Unsupervised Environment Design with a Learned Adversary
As a result, we make it possible for PAIRED to match or exceed state-of-the-art methods, producing robust agents in several established challenging procedurally-generated environments, including a partially-observed maze navigation task and a continuous-control car racing environment.
