
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Training models with discrete latent variables is challenging due to the...
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Conservative QLearning for Offline Reinforcement Learning
Effectively leveraging large, previously collected datasets in reinforce...
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
In this tutorial article, we aim to provide the reader with the conceptu...
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D4RL: Datasets for Deep DataDriven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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Datasets for DataDriven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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MetaLearning without Memorization
The ability to learn new concepts with small amounts of data is a critic...
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Behavior Regularized Offline Reinforcement Learning
In reinforcement learning (RL) research, it is common to assume access t...
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Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
Posterior collapse in Variational Autoencoders (VAEs) arises when the va...
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EnergyInspired Models: Learning with SamplerInduced Distributions
Energybased models (EBMs) are powerful probabilistic models, but suffer...
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Reinforcement Learning Driven Heuristic Optimization
Heuristic algorithms such as simulated annealing, Concorde, and METIS ar...
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Stabilizing OffPolicy QLearning via Bootstrapping Error Reduction
Offpolicy reinforcement learning aims to leverage experience collected ...
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On Variational Bounds of Mutual Information
Estimating and optimizing Mutual Information (MI) is core to many proble...
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ModelBased Reinforcement Learning for Atari
Modelfree reinforcement learning (RL) can be used to learn effective po...
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Learning to Walk via Deep Reinforcement Learning
Deep reinforcement learning suggests the promise of fully automated lear...
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Soft ActorCritic Algorithms and Applications
Modelfree deep reinforcement learning (RL) algorithms have been success...
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The Laplacian in RL: Learning Representations with Efficient Approximations
The smallest eigenvectors of the graph Laplacian are wellknown to provi...
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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Deep latent variable models have become a popular model choice due to th...
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SampleEfficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Integrating modelfree and modelbased approaches in reinforcement learn...
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Guided evolutionary strategies: escaping the curse of dimensionality in random search
Many applications in machine learning require optimizing a function whos...
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Smoothed Action Value Functions for Learning Gaussian Policies
Stateaction value functions (i.e., Qvalues) are ubiquitous in reinforc...
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The Mirage of ActionDependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of modelfree reinforcem...
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Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Recent advances in deep reinforcement learning have made significant str...
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An online sequencetosequence model for noisy speech recognition
Generative models have long been the dominant approach for speech recogn...
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Filtering Variational Objectives
When used as a surrogate objective for maximum likelihood estimation in ...
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Learning Hard Alignments with Variational Inference
There has recently been significant interest in hard attention models fo...
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MaxPooling Loss Training of Long ShortTerm Memory Networks for SmallFootprint Keyword Spotting
We propose a maxpooling based loss function for training Long ShortTer...
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REBAR: Lowvariance, unbiased gradient estimates for discrete latent variable models
Learning in models with discrete latent variables is challenging due to ...
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Regularizing Neural Networks by Penalizing Confident Output Distributions
We systematically explore regularizing neural networks by penalizing low...
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Compacting Neural Network Classifiers via Dropout Training
We introduce dropout compaction, a novel method for training feedforwar...
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