Journal ArticleDOI
Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system
TLDR
In this article , a reinforcement learning (RL)-based control approach that uses a combination of a deep Q-learning (DQL) algorithm and a metaheuristic Gravitational search algorithm (GSA) is presented.About:
This article is published in Information Sciences.The article was published on 2022-01-01. It has received 80 citations till now. The article focuses on the topics: Reinforcement learning & Computer science.read more
Citations
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Journal ArticleDOI
A New Meta-Heuristics Data Clustering Algorithm Based on Tabu Search and Adaptive Search Memory
TL;DR: A new meta-heuristics algorithm called (MHTSASM) is proposed in this paper for data clustering, which is based on Tabu Search and K-M and indicates the superiority of the MH TSASM algorithm compared with other multiple clustering algorithms.
Journal ArticleDOI
Reinforcement learning based adaptive PID controller design for control of linear/nonlinear unstable processes
TL;DR: In this article , a generic data driven modified Proximal Policy Optimization (m-PPO) reinforcement learning based adaptive PID controller (RL-PID) was developed for the control of open loop unstable processes.
Journal ArticleDOI
A meta-inspired termite queen algorithm for global optimization and engineering design problems
TL;DR: In this paper , a bio-inspired termite queen algorithm (TQA) is proposed to solve optimization problems by simulating the division of labor in termite populations under a queen's rule.
Journal ArticleDOI
An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning
TL;DR: A multi-agent GAIL (MAG) algorithm is proposed, based on the generative adversarial imitation (GAIL) algorithm combined with a multi- agent, that enables the AUV to directly learn from expert demonstrations, overcoming the difficulty of slow initial training of the network.
Journal ArticleDOI
Performance-based data-driven optimal tracking control of shape memory alloy actuated manipulator through reinforcement learning
TL;DR: In this paper , a continuous-time optimal tracking control problem of a shape memory alloy (SMA) actuated manipulator subject to prescribed error constraints and completely unknown nonlinear dynamics is investigated.
References
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Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
GSA: A Gravitational Search Algorithm
TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
Journal ArticleDOI
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
Journal ArticleDOI
Deep Reinforcement Learning: A Brief Survey
TL;DR: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world as discussed by the authors.
Journal ArticleDOI
Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
TL;DR: In this article, the authors describe the use of reinforcement learning to design feedback controllers for discrete and continuous-time dynamical systems that combine features of adaptive control and optimal control, which are not usually designed to be optimal in the sense of minimizing user-prescribed performance functions.
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