Open AccessPosted Content
Value Iteration Networks
TLDR
The Value Iteration Network (VIN) as discussed by the authors is a differentiable approximation of the value iteration algorithm, which can be represented as a convolutional neural network and trained end-to-end using standard backpropagation.Abstract:
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.read more
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
TL;DR: This tutorial article aims to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms: reinforcementlearning algorithms that utilize previously collected data, without additional online data collection.
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Deep Reinforcement Learning: An Overview
TL;DR: This work discusses core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration, and important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn.
Journal ArticleDOI
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Julian Schrittwieser,Ioannis Antonoglou,Thomas Hubert,Karen Simonyan,Laurent Sifre,Simon Schmitt,Arthur Guez,Edward Lockhart,Demis Hassabis,Thore Graepel,Timothy P. Lillicrap,David Silver +11 more
TL;DR: The MuZero algorithm is presented, which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics.
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Learning to reinforcement learn
Jane X. Wang,Zeb Kurth-Nelson,Dhruva Tirumala,Hubert Soyer,Joel Z. Leibo,Rémi Munos,Charles Blundell,Dharshan Kumaran,Matthew Botvinick +8 more
TL;DR: Deep Meta-Reinforcement Learning (DML) as discussed by the authors is a meta-learning approach for reinforcement learning, where the learned RL algorithm can differ from the original one in arbitrary ways and is configured to exploit structure in the training domain.
Journal ArticleDOI
Toward an Integration of Deep Learning and Neuroscience.
TL;DR: In this paper, the authors argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes.
References
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
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