Publications (15)
AI and the Future of Digital Public Squares
Beth Goldberg, Diana Acosta-Navas, Michiel Bakker +24
Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large lan…
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Kevin R. McKee, Edward Hughes, Tina O. Zhu +7
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate. Laboratory experiments have extensively explored the first part of this pr…
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst +24
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, i…
Attention over learned object embeddings enables complex visual reasoning
David Ding, Felix Hill, Adam Santoro +2
Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning. On these more challenging ta…
HiP: Hierarchical Perceiver
Joao Carreira, Skanda Koppula, Daniel Zoran +10
General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this gene…
Collaborating with Humans without Human Data
DJ Strouse, Kevin R. McKee, Matt Botvinick +2
Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning tech…
Learning to Share and Hide Intentions using Information Regularization
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum +2
Learning to cooperate with friends and compete with foes is a key component of multi-agent reinforcement learning. Typically to do so, one requires access to either a model of or i…
Learning to Learn without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo +4
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfe…
Probing Physics Knowledge Using Tools from Developmental Psychology
Luis Piloto, Ari Weinstein, Dhruva TB +6
In order to build agents with a rich understanding of their environment, one key objective is to endow them with a grasp of intuitive physics; an ability to reason about three-dime…
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
Raphael Koster, Miruna Pîslar, Andrea Tacchetti +7
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves.…
Unsupervised Predictive Memory in a Goal-Directed Agent
Greg Wayne, Chia-Chun Hung, David Amos +21
Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of import…
Synthetic Returns for Long-Term Credit Assignment
David Raposo, Sam Ritter, Adam Santoro +5
Since the earliest days of reinforcement learning, the workhorse method for assigning credit to actions over time has been temporal-difference (TD) learning, which propagates credi…
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…
MONet: Unsupervised Scene Decomposition and Representation
Christopher P. Burgess, Loic Matthey, Nicholas Watters +4
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interaction…
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala +6
In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applicati…