32 citations · 60 across the 7 of their papers we have counts for
9 papers
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning
Gheorghe Comanici, Amelia Glaese, Anita Gergely +5
Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lowe…
Temporally Abstract Partial Models
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici +1
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement learning, option models (Sutton, Precup \…
AndroidEnv: A Reinforcement Learning Platform for Android
Daniel Toyama, Philippe Hamel, Anita Gergely +6
We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to interact with a wide…
Training a First-Order Theorem Prover from Synthetic Data
Vlad Firoiu, Eser Aygun, Ankit Anand +6
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.…
What can I do here? A Theory of Affordances in Reinforcement Learning
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici +2
Reinforcement learning algorithms usually assume that all actions are always available to an agent. However, both people and animals understand the general link between the feature…
Learning to Prove from Synthetic Theorems
Eser Aygün, Zafarali Ahmed, Ankit Anand +5
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.…