The applicability of reinforcement learning methods in the development of industry 4.0 applications
Reinforcement learning (RL) methods can successfully solve complex optimization
problems. Our article gives a systematic overview of major types of RL methods, their …
problems. Our article gives a systematic overview of major types of RL methods, their …
Deep reinforcement learning based home energy management system with devices operational dependencies
Advanced metering infrastructure and bilateral communication technologies facilitate the
development of the home energy management system in the smart home. In this paper, we …
development of the home energy management system in the smart home. In this paper, we …
Adaptive exploration policy for exploration–exploitation tradeoff in continuous action control optimization
M Li, T Huang, W Zhu - International Journal of Machine Learning and …, 2021 - Springer
The optimization of continuous action control is an important research field. It aims to find
optimal decisions by the experience of making decisions in a continuous action control task …
optimal decisions by the experience of making decisions in a continuous action control task …
A content search method for security topics in microblog based on deep reinforcement learning
N Zhou, J Du, X Yao, W Cui, Z Xue, M Liang - World Wide Web, 2020 - Springer
Traditional methods treat the search problem as a process of selecting and ranking
sequential documents. The methods have been proved effective and are widely used in the …
sequential documents. The methods have been proved effective and are widely used in the …
Reinforcement learning for multi-agent with asynchronous missing information fusion method
Most current research on multi-agent reinforcement learning assumes a reliable
environment where agents have globally accurate observations. However, this assumption …
environment where agents have globally accurate observations. However, this assumption …
Exploration Decay Policy (EDP) to Enhanced Exploration-Exploitation Trade-Off in DDPG for Continuous Action Control Optimization
The optimization of continuous action control tasks is a crucial step in deep reinforcement
learning (DRL) applications. The goal is to identify optimal actions through the accumulation …
learning (DRL) applications. The goal is to identify optimal actions through the accumulation …
Learn to navigate through deep neural networks
K Wu - 2020 - dr.ntu.edu.sg
Autonomous navigation is a crucial prerequisite for mobile robots to perform various tasks
while it remains a great challenge due to its inherent complexity. This thesis deals with the …
while it remains a great challenge due to its inherent complexity. This thesis deals with the …
Multi-agent system for efficient decentralized information aggregation by modeling other agents' behavior
RD Vallam, R Pimplikar, K Mukherjee… - US Patent …, 2022 - Google Patents
(57) ABSTRACT A computer-implemented method is disclosed which includes receiving an
initial decision from each of a plurality of agents based on the same criteria, wherein at least …
initial decision from each of a plurality of agents based on the same criteria, wherein at least …