Analysing Deep Reinforcement Learning Agents Trained with Domain
Randomisation

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Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation

18 December 2019

Kai Arulkumaran

Feryal M. P. Behbahani

Anil Anthony Bharath

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Papers citing "Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation"

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Title
FADConv: A Frequency-Aware Dynamic Convolution for Farmland Non-agriculturalization Identification and Segmentation Tan Shu Li Shen 259 13 0 04 Apr 2025
Enabling robots to follow abstract instructions and complete complex dynamic tasks Ruaridh Mon-Williams Gen Li Ran Long Wenqian Du Chris Lucas LM&Ro 236 2 0 17 Jun 2024
Understanding What Affects Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence Jiafei Lyu Le Wan Xiu Li Zongqing Lu CML OffRL 273 2 0 05 Feb 2024
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning Arthur Muller M. Sabatelli 128 5 0 21 Jul 2023
Leveraging Systematic Knowledge of 2D TransformationsNeurocomputing (Neurocomputing), 2022 Jiachen Kang W. Jia Xiangjian He 265 4 0 02 Jun 2022
Investigating the Properties of Neural Network Representations in Reinforcement LearningArtificial Intelligence (AIJ), 2022 Zheng Chen Erfan Miahi Martha White Marlos C. Machado Zaheer Abbas Raksha Kumaraswamy Vincent Liu Adam White 255 35 0 30 Mar 2022
Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning L. Pullum 370 6 0 22 Mar 2022
Robot Learning from Randomized Simulations: A ReviewFrontiers in Robotics and AI (Front. Robot. AI), 2021 Fabio Muratore Fabio Ramos Greg Turk Wenhao Yu Michael Gienger Jan Peters AI4CE 248 106 0 01 Nov 2021
Validate on Sim, Detect on Real -- Model Selection for Domain RandomizationIEEE International Conference on Robotics and Automation (ICRA), 2021 Gal Leibovich Guy Jacob Shadi Endrawis Gal Novik Aviv Tamar 246 7 0 01 Nov 2021