Reinforcement learning based routing in networks: Review and classification of approaches
Z Mammeri - Ieee Access, 2019 - ieeexplore.ieee.org
Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …
which a system can learn from its previous interactions with its environment to efficiently …
Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues
In wireless networks, context awareness and intelligence are capabilities that enable each
host to observe, learn, and respond to its complex and dynamic operating environment in an …
host to observe, learn, and respond to its complex and dynamic operating environment in an …
[图书][B] A concise introduction to decentralized POMDPs
FA Oliehoek, C Amato - 2016 - Springer
This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …
cooperative systems. It is aimed at graduate students and researchers in the fields of …
Machine learning meets communication networks: Current trends and future challenges
The growing network density and unprecedented increase in network traffic, caused by the
massively expanding number of connected devices and online services, require intelligent …
massively expanding number of connected devices and online services, require intelligent …
Self-optimizing memory controllers: A reinforcement learning approach
Efficiently utilizing off-chip DRAM bandwidth is a critical issuein designing cost-effective,
high-performance chip multiprocessors (CMPs). Conventional memory controllers deliver …
high-performance chip multiprocessors (CMPs). Conventional memory controllers deliver …
If multi-agent learning is the answer, what is the question?
Y Shoham, R Powers, T Grenager - Artificial intelligence, 2007 - Elsevier
The area of learning in multi-agent systems is today one of the most fertile grounds for
interaction between game theory and artificial intelligence. We focus on the foundational …
interaction between game theory and artificial intelligence. We focus on the foundational …
Decentralized pomdps
FA Oliehoek - Reinforcement learning: state-of-the-art, 2012 - Springer
This chapter presents an overview of the decentralized POMDP (Dec-POMDP) framework. In
a Dec-POMDP, a team of agents collaborates to maximize a global reward based on local …
a Dec-POMDP, a team of agents collaborates to maximize a global reward based on local …
Application of reinforcement learning to routing in distributed wireless networks: a review
HAA Al-Rawi, MA Ng, KLA Yau - Artificial Intelligence Review, 2015 - Springer
The dynamicity of distributed wireless networks caused by node mobility, dynamic network
topology, and others has been a major challenge to routing in such networks. In the …
topology, and others has been a major challenge to routing in such networks. In the …
On learning in collective self-adaptive systems: State of practice and a 3d framework
M D'Angelo, S Gerasimou… - 2019 IEEE/ACM 14th …, 2019 - ieeexplore.ieee.org
Collective self-adaptive systems (CSAS) are distributed and interconnected systems
composed of multiple agents that can perform complex tasks such as environmental data …
composed of multiple agents that can perform complex tasks such as environmental data …
AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning
Flying ad hoc networks (FANETs), as the emerging communication paradigm, have been
widely used in civil and military fields. Packet routing in FANETs is challenging due to …
widely used in civil and military fields. Packet routing in FANETs is challenging due to …