1 code implementation • 30 Sep 2023 • Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations.
no code implementations • 24 Jan 2023 • Safa Alver, Doina Precup
Learning models of the environment from pure interaction is often considered an essential component of building lifelong reinforcement learning agents.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 16 Jun 2022 • Safa Alver, Doina Precup
After viewing them through the lens of dynamic programming, we first consider the classical instantiations of these planning styles and provide theoretical results and hypotheses on which one will perform better in the pure planning, planning & learning, and transfer learning settings.
Model-based Reinforcement Learning reinforcement-learning +2
no code implementations • ICLR 2022 • Safa Alver, Doina Precup
We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks with no or very little new data.
no code implementations • 29 Apr 2021 • Safa Alver, Doina Precup
Recurrent meta reinforcement learning (meta-RL) agents are agents that employ a recurrent neural network (RNN) for the purpose of "learning a learning algorithm".
no code implementations • ICML Workshop LifelongML 2020 • Safa Alver, Doina Precup
Due to the realization that deep reinforcement learning algorithms trained on high-dimensional tasks can strongly overfit to their training environments, there have been several studies that investigated the generalization performance of these algorithms.
1 code implementation • 10 Aug 2019 • Safa Alver, Ugur Halici
Visual face tracking is one of the most important tasks in video surveillance systems.