Machine learning into metaheuristics: A survey and taxonomy
EG Talbi - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …
effective, and robust metaheuristics has become increasingly popular. Many of those …
The role of information structures in game-theoretic multi-agent learning
Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively
from their experience when interacting with other agents in dynamic environments. The …
from their experience when interacting with other agents in dynamic environments. The …
An analysis of the robustness of UAV agriculture field coverage using multi-agent reinforcement learning
Agriculture is a vital sector in develo** nations such as India, and the use of autonomous
vehicles and Internet of Things (IoT) technology has the potential to revolutionize farming …
vehicles and Internet of Things (IoT) technology has the potential to revolutionize farming …
Robust deep reinforcement learning with adversarial attacks
A Pattanaik, Z Tang, S Liu, G Bommannan… - arxiv preprint arxiv …, 2017 - arxiv.org
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves
the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter …
the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter …
Towards generalization and simplicity in continuous control
The remarkable successes of deep learning in speech recognition and computer vision
have motivated efforts to adapt similar techniques to other problem domains, including …
have motivated efforts to adapt similar techniques to other problem domains, including …
Optimal and scalable caching for 5G using reinforcement learning of space-time popularities
A Sadeghi, F Sheikholeslami… - IEEE Journal of …, 2017 - ieeexplore.ieee.org
Small basestations (SBs) equipped with caching units have potential to handle the
unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul …
unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul …
Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms
MM Drugan - Swarm and evolutionary computation, 2019 - Elsevier
A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …
Optimal and fast real-time resource slicing with deep dueling neural networks
Effective network slicing requires an infrastructure/network provider to deal with the
uncertain demands and real-time dynamics of the network resource requests. Another …
uncertain demands and real-time dynamics of the network resource requests. Another …
Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits
It has been recently shown that general policies for many classical planning domains can be
expressed and learned in terms of a pool of features defined from the domain predicates …
expressed and learned in terms of a pool of features defined from the domain predicates …
Reinforcement learning for real-time optimization in NB-IoT networks
NarrowBand Internet of Things (NB-IoT) is an emerging cellular-based technology that offers
a range of flexible configurations for massive IoT radio access from groups of devices with …
a range of flexible configurations for massive IoT radio access from groups of devices with …