A review on reinforcement learning: Introduction and applications in industrial process control
In recent years, reinforcement learning (RL) has attracted significant attention from both
industry and academia due to its success in solving some complex problems. This paper …
industry and academia due to its success in solving some complex problems. This paper …
Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions
Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …
past decade and has provided effective control strategies in high-dimensional and non …
Policy iteration reinforcement learning-based control using a grey wolf optimizer algorithm
This paper presents a new Reinforcement Learning (RL)-based control approach that uses
the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the …
the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the …
Neural network-based control using actor-critic reinforcement learning and grey wolf optimizer with experimental servo system validation
This paper introduces a novel reference tracking control approach implemented using a
combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf …
combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf …
Reinforcement learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system
This paper presents a novel Reinforcement Learning (RL)-based control approach that uses
a combination of a Deep Q-Learning (DQL) algorithm and a metaheuristic Gravitational …
a combination of a Deep Q-Learning (DQL) algorithm and a metaheuristic Gravitational …
Advances and opportunities in machine learning for process data analytics
In this paper we introduce the current thrust of development in machine learning and
artificial intelligence, fueled by advances in statistical learning theory over the last 20 years …
artificial intelligence, fueled by advances in statistical learning theory over the last 20 years …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Survey of deep learning paradigms for speech processing
KB Bhangale, M Kothandaraman - Wireless Personal Communications, 2022 - Springer
Over the past decades, a particular focus is given to research on machine learning
techniques for speech processing applications. However, in the past few years, research …
techniques for speech processing applications. However, in the past few years, research …
Reinforcement learning–overview of recent progress and implications for process control
This paper provides an introduction to Reinforcement Learning (RL) technology,
summarizes recent developments in this area, and discusses their potential implications for …
summarizes recent developments in this area, and discusses their potential implications for …