A survey on ensemble learning
Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …
learning methods may fail to obtain satisfactory performances when dealing with complex …
Scalar reward is not enough: A response to silver, singh, precup and sutton (2021)
The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
Multi-objective multi-agent decision making: a utility-based analysis and survey
The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …
respect to a single objective, despite the fact that many real-world problem domains are …
Multi-objective Optimization of Space-Air-Ground Integrated Network Slicing Relying on a Pair of Central and Distributed Learning Algorithms
As an attractive enabling technology for next-generation wireless communications, network
slicing supports diverse customized services in the global space–air–ground-integrated …
slicing supports diverse customized services in the global space–air–ground-integrated …
Joint multi-objective optimization for radio access network slicing using multi-agent deep reinforcement learning
Radio access network (RAN) slices can provide various customized services for next-
generation wireless networks. Thus, multiple performance metrics of different types of RAN …
generation wireless networks. Thus, multiple performance metrics of different types of RAN …
Meta-learning for multi-objective reinforcement learning
Multi-objective reinforcement learning (MORL) is the generalization of standard
reinforcement learning (RL) approaches to solve sequential decision making problems that …
reinforcement learning (RL) approaches to solve sequential decision making problems that …
Multi-objective deep reinforcement learning for emergency scheduling in a water distribution network
In recent years, water contamination incidents have happened frequently, causing serious
losses and impacts on society. Therefore, how to quickly respond to emergency pollution …
losses and impacts on society. Therefore, how to quickly respond to emergency pollution …
Adaptive traffic signal control system using composite reward architecture based deep reinforcement learning
ARM Jamil, KK Ganguly… - IET Intelligent Transport …, 2020 - Wiley Online Library
The increasing traffic congestion problem can be solved by an adaptive traffic signal control
(ATSC) system as it utilises real‐time traffic information to control traffic signals. Recently …
(ATSC) system as it utilises real‐time traffic information to control traffic signals. Recently …
A conceptual framework for externally-influenced agents: An assisted reinforcement learning review
A long-term goal of reinforcement learning agents is to be able to perform tasks in complex
real-world scenarios. The use of external information is one way of scaling agents to more …
real-world scenarios. The use of external information is one way of scaling agents to more …
Multiobjectivization of single-objective optimization in evolutionary computation: a survey
Multiobjectivization has emerged as a new promising paradigm to solve single-objective
optimization problems (SOPs) in evolutionary computation, where an SOP is transformed …
optimization problems (SOPs) in evolutionary computation, where an SOP is transformed …