Towards a theory of out-of-distribution learning A Geisa, R Mehta, HS Helm, J Dey, E Eaton, J Dick, CE Priebe, ... arXiv preprint arXiv:2109.14501, 2021 | 16 | 2021 |
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture P Ladosz, E Ben-Iwhiwhu, J Dick, Y Hu, N Ketz, S Kolouri, JL Krichmar, ... arXiv preprint arXiv:1909.09902, 2019 | 16 | 2019 |
Context meta-reinforcement learning via neuromodulation E Ben-Iwhiwhu, J Dick, NA Ketz, PK Pilly, A Soltoggio Neural Networks 152, 70-79, 2022 | 10 | 2022 |
Evolving inborn knowledge for fast adaptation in dynamic pomdp problems E Ben-Iwhiwhu, P Ladosz, J Dick, WH Chen, P Pilly, A Soltoggio Proceedings of the 2020 genetic and evolutionary computation conference, 280-288, 2020 | 10 | 2020 |
Reducing the ambiguity of Parikh matrices J Dick, LK Hutchinson, R Mercaş, D Reidenbach International Conference on Language and Automata Theory and Applications …, 2020 | 5 | 2020 |
Detecting changes and avoiding catastrophic forgetting in dynamic partially observable environments J Dick, P Ladosz, E Ben-Iwhiwhu, H Shimadzu, P Kinnell, PK Pilly, ... Frontiers in neurorobotics 14, 578675, 2020 | 4 | 2020 |
The configurable tree graph (ct-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning A Soltoggio, E Ben-Iwhiwhu, C Peridis, P Ladosz, J Dick, PK Pilly, ... arXiv preprint arXiv:2302.10887, 2023 | 3 | 2023 |
Statistical Context Detection for Deep Lifelong Reinforcement Learning J Dick, S Nath, C Peridis, E Benjamin, S Kolouri, A Soltoggio arXiv preprint arXiv:2405.19047, 2024 | | 2024 |