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07/05/2022
An Empirical Study of Implicit Regularization in Deep Offline RL
Deep neural networks are the most commonly used function approximators i...
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04/21/2022
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Actor-critic algorithms that make use of distributional policy evaluatio...
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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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06/01/2020
Acme: A Research Framework for Distributed Reinforcement Learning
Deep reinforcement learning has led to many recent-and groundbreaking-ad...
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10/22/2019
Improving the Gating Mechanism of Recurrent Neural Networks
Gating mechanisms are widely used in neural network models, where they a...
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09/03/2019
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonst...
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06/20/2018
Synthesizing Diverse, High-Quality Audio Textures
Texture synthesis techniques based on matching the Gram matrix of featur...
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11/28/2017
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability ...
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06/03/2015
Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets...
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06/29/2012
Stochastic Variational Inference
We develop stochastic variational inference, a scalable algorithm for ap...
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06/27/2012
Sparse Stochastic Inference for Latent Dirichlet allocation
We present a hybrid algorithm for Bayesian topic models that combines th...
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06/18/2012
