35 citations · 105 across the 13 of their papers we have counts for
13 papers
Data Valuation for Offline Reinforcement Learning
Amir Abolfazli, Gregory Palmer, Daniel Kudenko
The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions. In many re…
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domain…
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks
Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova +3
Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistan…
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
Learning to produce efficient movement behaviour for humanoid robots from scratch is a hard problem, as has been illustrated by the "Learning to run" competition at NIPS 2017. The…
A comparative evaluation of machine learning methods for robot navigation through human crowds
Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman
Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to co…
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
Ivan Sosin, Daniel Kudenko, Aleksei Shpilman
Movement control of artificial limbs has made big advances in recent years. New sensor and control technology enhanced the functionality and usefulness of artificial limbs to the p…