Dec 22, 2021 � Abstract:Deep reinforcement learning is actively used for training autonomous car policies in a simulated driving environment.
A benchmarking framework for the comparison of deep reinforcement learning in a vision-based autonomous driving will open up the possibilities for training�...
Sep 10, 2024 � Deep reinforcement learning is actively used for training autonomous driving agents in a vision-based urban simulated environment.
Our training, testing, and validation of AVs are performed using the urban driving simulation framework Carla [29]. Both RLlib and Carla are integrated by�...
Dec 22, 2021 � We provide a systematic evaluation and comparative analysis of 6 deep reinforcement learning algorithms for autonomous and adversarial driving in four-way�...
May 27, 2022 � Deep reinforcement learning is actively used for training autonomous andadversarial car policies in a simulated driving environment. Due to the�...
We demonstrate that the autonomous cars retrained using the effective adversarial inputs noticeably increase the performance of their driving policies in terms�...
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment. Authors: A. Sharif.
Missing: Adversarial | Show results with:Adversarial
Mar 19, 2024 � We propose a deep adversarial reinforcement learning based approach for robust autonomous driving in a roundabout passing scene.
Missing: Robustness | Show results with:Robustness
Dec 22, 2021 � In this paper, we propose a two-step methodology for autonomous cars that consists of (i) finding failure states in autonomous cars by training the adversarial�...
Missing: Evaluating | Show results with:Evaluating