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12/21/2022
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
Imitation learning (IL) is a simple and powerful way to use high-quality...
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11/28/2022
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes
The potential of offline reinforcement learning (RL) is that high-capaci...
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11/03/2022
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
In offline reinforcement learning (RL), a learner leverages prior logged...
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12/23/2021
Model Selection in Batch Policy Optimization
We study the problem of model selection in batch policy optimization: gi...
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12/09/2021
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Despite overparameterization, deep networks trained via supervised learn...
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06/15/2021
Coupled Gradient Estimators for Discrete Latent Variables
Training models with discrete latent variables is challenging due to the...
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04/28/2021
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Standard dynamics models for continuous control make use of feedforward ...
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03/30/2021
Benchmarks for Deep Off-Policy Evaluation
Off-policy evaluation (OPE) holds the promise of being able to leverage ...
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12/12/2020
Offline Policy Selection under Uncertainty
The presence of uncertainty in policy evaluation significantly complicat...
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06/24/2020
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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06/18/2020
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Training models with discrete latent variables is challenging due to the...
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06/08/2020
Conservative Q-Learning for Offline Reinforcement Learning
Effectively leveraging large, previously collected datasets in reinforce...
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05/04/2020
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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04/15/2020
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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04/15/2020
Datasets for Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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12/09/2019
Meta-Learning without Memorization
The ability to learn new concepts with small amounts of data is a critic...
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11/26/2019
Behavior Regularized Offline Reinforcement Learning
In reinforcement learning (RL) research, it is common to assume access t...
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11/06/2019
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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10/31/2019
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Energy-based models (EBMs) are powerful probabilistic models, but suffer...
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06/16/2019
Reinforcement Learning Driven Heuristic Optimization
Heuristic algorithms such as simulated annealing, Concorde, and METIS ar...
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06/03/2019
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Off-policy reinforcement learning aims to leverage experience collected ...
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05/16/2019
On Variational Bounds of Mutual Information
Estimating and optimizing Mutual Information (MI) is core to many proble...
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03/01/2019
Model-Based Reinforcement Learning for Atari
Model-free reinforcement learning (RL) can be used to learn effective po...
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12/26/2018
Learning to Walk via Deep Reinforcement Learning
Deep reinforcement learning suggests the promise of fully automated lear...
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12/13/2018
Soft Actor-Critic Algorithms and Applications
Model-free deep reinforcement learning (RL) algorithms have been success...
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10/10/2018
The Laplacian in RL: Learning Representations with Efficient Approximations
The smallest eigenvectors of the graph Laplacian are well-known to provi...
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10/09/2018
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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07/04/2018
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Integrating model-free and model-based approaches in reinforcement learn...
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06/26/2018
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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03/06/2018
Smoothed Action Value Functions for Learning Gaussian Policies
State-action value functions (i.e., Q-values) are ubiquitous in reinforc...
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02/27/2018
The Mirage of Action-Dependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of model-free reinforcem...
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02/26/2018
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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06/16/2017
An online sequence-to-sequence model for noisy speech recognition
Generative models have long been the dominant approach for speech recogn...
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05/25/2017
Filtering Variational Objectives
When used as a surrogate objective for maximum likelihood estimation in ...
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05/16/2017
Learning Hard Alignments with Variational Inference
There has recently been significant interest in hard attention models fo...
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05/05/2017
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
We propose a max-pooling based loss function for training Long Short-Ter...
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03/21/2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
Learning in models with discrete latent variables is challenging due to ...
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01/23/2017
Regularizing Neural Networks by Penalizing Confident Output Distributions
We systematically explore regularizing neural networks by penalizing low...
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11/18/2016
