150 citations · 322 across the 8 of their papers we have counts for
7 papers · 1 filter
Solving Reasoning Tasks with a Slot Transformer
Ryan Faulkner, Daniel Zoran
The ability to carve the world into useful abstractions in order to reason about time and space is a crucial component of intelligence. In order to successfully perceive and act ef…
Rapid Task-Solving in Novel Environments
Sam Ritter, Ryan Faulkner, Laurent Sartran +3
We propose the challenge of rapid task-solving in novel environments (RTS), wherein an agent must solve a series of tasks as rapidly as possible in an unfamiliar environment. An ef…
Generalization of Reinforcement Learners with Working and Episodic Memory
Meire Fortunato, Melissa Tan, Ryan Faulkner +6
Memory is an important aspect of intelligence and plays a role in many deep reinforcement learning models. However, little progress has been made in understanding when specific mem…
OpenSpiel: A Framework for Reinforcement Learning in Games
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau +24
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi…
Interval timing in deep reinforcement learning agents
Ben Deverett, Ryan Faulkner, Meire Fortunato +2
The measurement of time is central to intelligent behavior. We know that both animals and artificial agents can successfully use temporal dependencies to select actions. In artific…
Relational recurrent neural networks
Adam Santoro, Ryan Faulkner, David Raposo +7
Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to per…
Dyna Planning using a Feature Based Generative Model
Ryan Faulkner, Doina Precup
Dyna-style reinforcement learning is a powerful approach for problems where not much real data is available. The main idea is to supplement real trajectories, or sequences of sampl…