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02/17/2022
Retrieval-Augmented Reinforcement Learning
Most deep reinforcement learning (RL) algorithms distill experience into...
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10/07/2021
Evaluating model-based planning and planner amortization for continuous control
There is a widespread intuition that model-based control methods should ...
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10/03/2020
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
Intelligent robots need to achieve abstract objectives using concrete, s...
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09/11/2020
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Recent work in deep reinforcement learning (RL) has produced algorithms ...
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01/11/2019
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challeng...
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10/15/2018
Optimizing Agent Behavior over Long Time Scales by Transporting Value
Humans spend a remarkable fraction of waking life engaged in acts of "me...
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04/03/2018
Probing Physics Knowledge Using Tools from Developmental Psychology
In order to build agents with a rich understanding of their environment,...
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03/28/2018
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and s...
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12/12/2016
Generalizable Features From Unsupervised Learning
Humans learn a predictive model of the world and use this model to reaso...
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05/09/2016
Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
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02/04/2016
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep rein...
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03/05/2015
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to...
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11/06/2014
Conditional Generative Adversarial Nets
Generative Adversarial Nets [8] were recently introduced as a novel way ...
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06/10/2014
Generative Adversarial Networks
We propose a new framework for estimating generative models via an adver...
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12/21/2013
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Catastrophic forgetting is a problem faced by many machine learning mode...
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08/20/2013
Pylearn2: a machine learning research library
Pylearn2 is a machine learning research library. This does not just mean...
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07/01/2013
Challenges in Representation Learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning focused ...
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02/18/2013
