Search Results for author: Matt Botvinick

Found 13 papers, 7 papers with code

Hierarchical Perceiver

no code implementations22 Feb 2022 Joao Carreira, Skanda Koppula, Daniel Zoran, Adria Recasens, Catalin Ionescu, Olivier Henaff, Evan Shelhamer, Relja Arandjelovic, Matt Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle

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.

Collaborating with Humans without Human Data

no code implementations NeurIPS 2021 DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett

Here, we study the problem of how to train agents that collaborate well with human partners without using human data.

Multi-agent Reinforcement Learning

Synthetic Returns for Long-Term Credit Assignment

2 code implementations24 Feb 2021 David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt Botvinick, Hado van Hasselt, Francis Song

We propose state-associative (SA) learning, where the agent learns associations between states and arbitrarily distant future rewards, then propagates credit directly between the two.

Attention over learned object embeddings enables complex visual reasoning

1 code implementation NeurIPS 2021 David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick

Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning.

Visual Reasoning

Rapid Task-Solving in Novel Environments

no code implementations ICLR 2021 Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick, David Raposo

We show that EPNs learn to execute a value iteration-like planning algorithm and that they generalize to situations beyond their training experience.

Probing Physics Knowledge Using Tools from Developmental Psychology

no code implementations3 Apr 2018 Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matt Botvinick

While some work on this problem has taken the approach of building in components such as ready-made physics engines, other research aims to extract general physical concepts directly from sensory data.

Learning to reinforcement learn

7 code implementations17 Nov 2016 Jane. X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, Matt Botvinick

We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.

Meta-Learning Meta Reinforcement Learning +1

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