DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation
We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of deep Q-learning network variants and hyperparameter configurations through large-scale, open competition. This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space.
Code (6)
Tasks
Autonomous DrivingAutonomous NavigationDeep Reinforcement LearningQ-Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic
Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or suppor…
Decision MakingDeep Reinforcement LearningReinforcement LearningTransfer LearningBayesian Optimization for Iterative Learning
The performance of deep (reinforcement) learning systems crucially depends on the choice of hyperparameters. Their tuning is notoriously expensive, typically requiring an iterative training process to run for numerous st…
Bayesian OptimizationDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
The performance of modern reinforcement learning algorithms critically relies on tuning ever-increasing numbers of hyperparameters. Often, small changes in a hyperparameter can lead to drastic changes in performance, and…
reinforcement-learningReinforcement LearningSensitivityRLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting
Hyperparameter tuning in 3D Gaussian Splatting (3DGS) is a labor-intensive and expert-driven process, often resulting in inconsistent reconstructions and suboptimal results. We propose RLGS, a plug-and-play reinforcement…
Reinforcement LearningOnline Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning
Reinforcement learning is a promising paradigm for learning robot control, allowing complex control policies to be learned without requiring a dynamics model. However, even state of the art algorithms can be difficult to…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)