no code implementations • 21 Apr 2022 • Gheorghe Comanici, Amelia Glaese, Anita Gergely, Daniel Toyama, Zafarali Ahmed, Tyler Jackson, Philippe Hamel, Doina Precup
While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving the performance of the same DQN agent over different levels of abstraction.
Hierarchical Reinforcement Learning reinforcement-learning +1
1 code implementation • NeurIPS 2021 • Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales.
no code implementations • NeurIPS 2019 • André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, Doina Precup
Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options.
2 code implementations • 27 May 2021 • Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, Doina Precup
We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem.
1 code implementation • ICML 2020 • Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup
Gibson (1977) coined the term "affordances" to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents.
no code implementations • NeurIPS 2015 • Gheorghe Comanici, Doina Precup, Prakash Panangaden
We provide a theoretical framework for analyzing basis function construction for linear value function approximation in Markov Decision Processes (MDPs).