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Dueling Network Architectures for Deep Reinforcement Learning
TLDR
This paper presents a new neural network architecture for model-free reinforcement learning that leads to better policy evaluation in the presence of many similar-valued actions and enables the RL agent to outperform the state-of-the-art on the Atari 2600 domain. Expand
An Introduction to MCMC for Machine Learning
TLDR
This purpose of this introductory paper is to introduce the Monte Carlo method with emphasis on probabilistic machine learning and review the main building blocks of modern Markov chain Monte Carlo simulation. Expand
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
TLDR
A tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions using the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. Expand
Sequential Monte Carlo Methods in Practice
TLDR
This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection. Expand
Taking the Human Out of the Loop: A Review of Bayesian Optimization
TLDR
This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications. Expand
The Unscented Particle Filter
TLDR
This paper proposes a new particle filter based on sequential importance sampling that outperforms standard particle filtering and other nonlinear filtering methods very substantially and is in agreement with the theoretical convergence proof for the algorithm. Expand
Learning to learn by gradient descent by gradient descent
TLDR
This paper shows how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Expand
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
TLDR
It is shown that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs, and are demonstrated on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. Expand
Matching Words and Pictures
TLDR
A new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text, is presented, and a number of models for the joint distribution of image regions and words are developed, including several which explicitly learn the correspondence between regions and Words. Expand
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
TLDR
By embracing deep neural networks, this work is able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. Expand
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