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Data Valuation for Offline Reinforcement Learning


May 19, 2022
Amir Abolfazli, Gregory Palmer, Daniel Kudenko

* 9 pages, 3 figures, 2 tables 

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MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning


Dec 17, 2020
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman

* arXiv admin note: substantial text overlap with arXiv:1811.12557 

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Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data


Dec 16, 2020
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman


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A comparative evaluation of machine learning methods for robot navigation through human crowds


Dec 16, 2020
Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman


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Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks


Dec 16, 2020
Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Anton Burakov, Elena Nadezhdina


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Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation


Dec 16, 2020
Ivan Sosin, Daniel Kudenko, Aleksei Shpilman


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Curriculum Learning with a Progression Function


Aug 02, 2020
Andrea Bassich, Francesco Foglino, Matteo Leonetti, Daniel Kudenko


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Deep Reinforcement Learning with Graph-based State Representations


Apr 29, 2020
Vikram Waradpande, Daniel Kudenko, Megha Khosla


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Uniform State Abstraction For Reinforcement Learning


Apr 06, 2020
John Burden, Daniel Kudenko

* 8 Pages, 2 figures, Accepted for publication in the European Conference of Artificial Intelligence (ECAI 2020) 

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Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems


Mar 13, 2019
Kleanthis Malialis, Sam Devlin, Daniel Kudenko

* Keywords: congestion problems, resource management, multiagent reinforcement learning, multiagent systems, multiagent learning, resource abstraction. In Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems (AAMAS '16) 

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