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PILCO: A Model-Based and Data-Efficient Approach to Policy Search
TLDR
PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way by learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning.
Doubly Stochastic Variational Inference for Deep Gaussian Processes
TLDR
This work presents a doubly stochastic variational inference algorithm, which does not force independence between layers in Deep Gaussian processes, and provides strong empirical evidence that the inference scheme for DGPs works well in practice in both classification and regression.
Deep Reinforcement Learning: A Brief Survey
TLDR
This survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic, and highlight the unique advantages of deep neural networks, focusing on visual understanding via RL.
A Survey on Policy Search for Robotics
TLDR
This work classifies model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and presents a unified view on existing algorithms.
Gaussian Processes for Data-Efficient Learning in Robotics and Control
TLDR
This paper learns a probabilistic, non-parametric Gaussian process transition model of the system and applies it to autonomous learning in real robot and control tasks, achieving an unprecedented speed of learning.
Distributed Gaussian Processes
TLDR
The robust Bayesian Committee Machine is introduced, a practical and scalable product-of-experts model for large-scale distributed GP regression and can be used on heterogeneous computing infrastructures, ranging from laptops to clusters.
A Brief Survey of Deep Reinforcement Learning
TLDR
This survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic, and highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning.
Efficient reinforcement learning using Gaussian processes
TLDR
First, PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available is introduced, and principled algorithms for robust filtering and smoothing in GP dynamic systems are proposed.
Manifold Gaussian Processes for regression
TLDR
Manifold Gaussian Processes is a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space, which allows to learn data representations, which are useful for the overall regression task.
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control
TLDR
This work proposes a model-based RL framework based on probabilistic Model Predictive Control based on Gaussian Processes to incorporate model uncertainty into long-term predictions, thereby, reducing the impact of model errors and provides theoretical guarantees for first-order optimality in the GP-based transition models with deterministic approximate inference for long- term planning.
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