Papers Car Racing
“Car Racing” 태그가 달린 논문 48편 · 필터 해제
Sample-Efficient Behavior Cloning Using General Domain Knowledge
Behavior cloning has shown success in many sequential decision-making tasks by learning from expert demonstrations, yet they can be very sample inefficient and fail to generalize to unseen scenarios. One approach to thes…
Car RacingFeature EngineeringGeneral KnowledgeSequential Decision MakingSelf-Driving Car Racing: Application of Deep Reinforcement Learning
This paper explores the application of deep reinforcement learning (RL) techniques in the domain of autonomous self-driving car racing. Motivated by the rise of AI-driven mobility and autonomous racing events, the projec…
AI AgentAutonomous DrivingAutonomous RacingCar Racing+6Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing
Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates must react to cues of a human teammate…
Car RacingPerceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games
Defining and measuring decision-making styles, also known as playstyles, is crucial in gaming, where these styles reflect a broad spectrum of individuality and diversity. However, finding a universally applicable measure…
Atari GamesBoard GamesCar RacingDecision Making+4Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors
Combining Reinforcement Learning (RL) with a prior controller can yield the best out of two worlds: RL can solve complex nonlinear problems, while the control prior ensures safer exploration and speeds up training. Prior…
Car RacingQ-LearningReinforcement Learning (RL)Distributionally Robust Constrained Reinforcement Learning under Strong Duality
We study the problem of Distributionally Robust Constrained RL (DRC-RL), where the goal is to maximize the expected reward subject to environmental distribution shifts and constraints. This setting captures situations wh…
Car Racingreinforcement-learningReinforcement LearningA Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end deep reinforcement learning agent met this…
Autonomous RacingCar RacingDeep Reinforcement LearningRaceLens: 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. The developed models have demonstrated their efficiency in a wide…
Car RacingEnd-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. In scenarios …
Autonomous RacingCar RacingDecision MakingNavigate+3Stabilizing Unsupervised Environment Design with a Learned Adversary
A key challenge in training generally-capable agents is the design of training tasks that facilitate broad generalization and robustness to environment variations. This challenge motivates the problem setting of Unsuperv…
Car Racingcontinuous-controlContinuous ControlReinforcement Learning (RL)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. In order to reduce the number of samples queried, we propose a modification to DAg…
Car RacingDecision-making and control with diffractive optical networks
The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. Diffractive optical networks provide a promising solution for …
Autonomous DrivingCar RacingDecision MakingDeep Reinforcement Learning+3CT-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. Motivated by this, we present the design of the Control-Tutored Deep Q-Networks (CT-DQN) algo…
Car RacingDeep Reinforcement LearningOpenAI Gymreinforcement-learning+2Event Tables for Efficient Experience Replay
Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems. However, uniform sampling from an ER buffer can lead to slow convergence and unstable asymptotic behaviors. This paper intro…
Car RacingDeep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint. We present a gradient descent, a Gauss-Newton method, a Newton meth…
Car RacingA 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. In t…
Car RacingMicroRacer: 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. The complexity of the environment has been explicitly calibrated to allow users to…
Car RacingDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1Feedback 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. A soft actor-critic approach is used to learn a decoupli…
Car Racingreinforcement-learningReinforcement LearningReinforcement Learning (RL)An Unsupervised Video Game Playstyle Metric via State Discretization
On playing video games, different players usually have their own playstyles. Recently, there have been great improvements for the video game AIs on the playing strength. However, past researches for analyzing the behavio…
Atari GamesCar RacingDecision MakingDeep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
Despite the potential of active inference for visual-based control, learning the model and the preferences (priors) while interacting with the environment is challenging. Here, we study the performance of a deep active i…
Car RacingQ-LearningRepresentation Learning