23 citations · 62 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
Uniform State Abstraction For Reinforcement Learning
John Burden, Daniel Kudenko
Potential Based Reward Shaping combined with a potential function based on appropriately defined abstract knowledge has been shown to significantly improve learning speed in Reinfo…
Graph-based State Representation for Deep Reinforcement Learning
Vikram Waradpande, Daniel Kudenko, Megha Khosla
Deep RL approaches build much of their success on the ability of the deep neural network to generate useful internal representations. Nevertheless, they suffer from a high sample-c…