39 citations · 60 across the 6 of their papers we have counts for
7 papers
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling
Ashish Kumar, Ilya Kuzovkin
Recent advances in batch (offline) reinforcement learning have shown promising results in learning from available offline data and proved offline reinforcement learning to be an es…
Understanding Information Processing in Human Brain by Interpreting Machine Learning Models
Ilya Kuzovkin
The thesis explores the role machine learning methods play in creating intuitive computational models of neural processing. Combined with interpretability techniques, machine learn…
OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research
Ashish Kumar, Toby Buckley, John B. Lanier +3
Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for the community. The challenge that the commun…
Direct information transfer rate optimisation for SSVEP-based BCI
Anti Ingel, Ilya Kuzovkin, Raul Vicente
In this work, a classification method for SSVEP-based BCI is proposed. The classification method uses features extracted by traditional SSVEP-based BCI methods and finds optimal di…
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
Himanshu Sahni, Toby Buckley, Pieter Abbeel +1
Reinforcement Learning (RL) algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (H…
Combining Static and Dynamic Features for Multivariate Sequence Classification
Anna Leontjeva, Ilya Kuzovkin
Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dict…
Multiagent Cooperation and Competition with Deep Reinforcement Learning
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja +5
Multiagent systems appear in most social, economical, and political situations. In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind t…