Search Results for author:
Found 13 papers, 9 papers with code
HiP: Hierarchical Perceiver
This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video.
Collaborating with Humans without Human Data
Here, we study the problem of how to train agents that collaborate well with human partners without using human data.
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Synthetic Returns for Long-Term Credit Assignment
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
Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning.
Ranked #4 on
Video Object Tracking
on CATER
Rapid Task-Solving in Novel Environments
We show that EPNs learn to execute a value iteration-like planning algorithm and that they generalize to situations beyond their training experience.
MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence.
Learning to Share and Hide Intentions using Information Regularization
We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Relational inductive biases, deep learning, and graph networks
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
Probing Physics Knowledge Using Tools from Developmental Psychology
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.
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
Learning to reinforcement learn
We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.
Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent.
