M
Marc Peter Deisenroth
Researcher at University College London
Publications - 167
Citations - 12412
Marc Peter Deisenroth is an academic researcher from University College London. The author has contributed to research in topics: Gaussian process & Reinforcement learning. The author has an hindex of 41, co-authored 154 publications receiving 9335 citations. Previous affiliations of Marc Peter Deisenroth include University of Cambridge & Osaka University.
Papers
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Journal ArticleDOI
Deep Reinforcement Learning: A Brief Survey
TL;DR: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world as discussed by the authors.
Journal ArticleDOI
A brief survey of deep reinforcement learning
TL;DR: 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.
Proceedings Article
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
TL;DR: 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.
Book
A Survey on Policy Search for Robotics
TL;DR: 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.
Journal ArticleDOI
Gaussian Processes for Data-Efficient Learning in Robotics and Control
TL;DR: 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.