How machine learning changes the nature of cyberattacks on IoT networks: A survey
The Internet of Things (IoT) has continued gaining in popularity and importance in everyday
life in recent years. However, this development does not only present advantages. Indeed …
life in recent years. However, this development does not only present advantages. Indeed …
Reinforcement learning: A survey
This paper surveys the field of reinforcement learning from a computer-science perspective.
It is written to be accessible to researchers familiar with machine learning. Both the historical …
It is written to be accessible to researchers familiar with machine learning. Both the historical …
Q-learning algorithms: A comprehensive classification and applications
Q-learning is arguably one of the most applied representative reinforcement learning
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …
approaches and one of the off-policy strategies. Since the emergence of Q-learning, many …
Reinforcement learning for disassembly system optimization problems: A survey
The disassembly complexity of end-of-life products increases continuously. Traditional
methods are facing difficulties in solving the decision-making and control problems of …
methods are facing difficulties in solving the decision-making and control problems of …
Q-learning
CJCH Watkins, P Dayan - Machine learning, 1992 - Springer
Abstract Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally
in controlled Markovian domains. It amounts to an incremental method for dynamic …
in controlled Markovian domains. It amounts to an incremental method for dynamic …
[图书][B] Reinforcement learning: An introduction
The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …
learning, one of the most active research areas in artificial intelligence. Reinforcement …
Self-improving reactive agents based on reinforcement learning, planning and teaching
LJ Lin - Machine learning, 1992 - Springer
To date, reinforcement learning has mostly been studied solving simple learning tasks.
Reinforcement learning methods that have been studied so far typically converge slowly …
Reinforcement learning methods that have been studied so far typically converge slowly …
[图书][B] Reinforcement learning for robots using neural networks
LJ Lin - 1992 - search.proquest.com
Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this
dissertation is to extend the state of the art of reinforcement learning and enable its …
dissertation is to extend the state of the art of reinforcement learning and enable its …
Improving generalization for temporal difference learning: The successor representation
P Dayan - Neural computation, 1993 - ieeexplore.ieee.org
Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes
particular constraints on good function approximators or representations. Appropriate …
particular constraints on good function approximators or representations. Appropriate …
Learning to act using real-time dynamic programming
Learning methods based on dynamic programming (DP) are receiving increasing attention
in artificial intelligence. Researchers have argued that DP provides the appropriate basis for …
in artificial intelligence. Researchers have argued that DP provides the appropriate basis for …