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08/24/2023
APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT
We study diverse skill discovery in reward-free environments, aiming to ...
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06/01/2021
Reward is enough for convex MDPs
Maximising a cumulative reward function that is Markov and stationary, i...
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10/27/2020
Behavior Priors for Efficient Reinforcement Learning
As we deploy reinforcement learning agents to solve increasingly challen...
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09/10/2020
Importance Weighted Policy Learning and Adaption
The ability to exploit prior experience to solve novel problems rapidly ...
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05/03/2019
Information asymmetry in KL-regularized RL
Many real world tasks exhibit rich structure that is repeated across dif...
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04/10/2018
Understanding disentangling in β-VAE
We present new intuitions and theoretical assessments of the emergence o...
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12/02/2016
Overcoming catastrophic forgetting in neural networks
The ability to learn tasks in a sequential fashion is crucial to the dev...
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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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07/01/2015
Natural Neural Networks
We introduce Natural Neural Networks, a novel family of algorithms that ...
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11/19/2014
Compress and Control
This paper describes a new information-theoretic policy evaluation techn...
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10/01/2014
Deep Tempering
Restricted Boltzmann Machines (RBMs) are one of the fundamental building...
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01/16/2013
Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines
This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm ...
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10/19/2012
Disentangling Factors of Variation via Generative Entangling
Here we propose a novel model family with the objective of learning to d...
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03/20/2012
On Training Deep Boltzmann Machines
The deep Boltzmann machine (DBM) has been an important development in th...
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12/15/2010
