Add to ORKGIf reinforcement learning (RL) techniques are to be used for "real world" dynamic system control, the problems of noise and plant disturbance will have to be addressed. This study investigates the effects of noise/disturbance on five different RL algorithms: Watkins' Q-Learning (QL); Barto, Sutton and Anderson 's Adaptive Heuristic Critic (AHC); Sammut and Law's modern variant of Michie and Chamber's BOXES algorithm; and two new algorithms developed during the course of this study. Both these new algorithms are conceptually related to QL; both algorithms, called P-Trace and Q-Trace respectively, provide for substantially faster learning than straight QL overall, and for dramatically faster learning (by up to a ...
peer reviewedThis paper compares reinforcement learning (RL) with model predictive control (MPC) in ...
This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapti...
Abstract: Reinforcement Learning (RL) has as its main objective to maximize the rewards of an object...
In the last few years, Reinforcement Learning (RL), also called adaptive (or approximate) dynamic pr...
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to opti...
The main goal of this thesis was the evaluation and implementation of two types of reinforcement lea...
Traditional feedback control methods are often model-based and the mathematical system models need t...
This paper presents two dierent methods to derive an optimal feedback for a linear system, based on ...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We design...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We design...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We design...
In this article, we propose a new reinforcement learning (RL) method for a system having continuous ...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
peer reviewedThis paper compares reinforcement learning (RL) with model predictive control (MPC) in ...
This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapti...
Abstract: Reinforcement Learning (RL) has as its main objective to maximize the rewards of an object...
In the last few years, Reinforcement Learning (RL), also called adaptive (or approximate) dynamic pr...
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to opti...
The main goal of this thesis was the evaluation and implementation of two types of reinforcement lea...
Traditional feedback control methods are often model-based and the mathematical system models need t...
This paper presents two dierent methods to derive an optimal feedback for a linear system, based on ...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We design...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We design...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We design...
In this article, we propose a new reinforcement learning (RL) method for a system having continuous ...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
peer reviewedThis paper compares reinforcement learning (RL) with model predictive control (MPC) in ...
This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapti...
Abstract: Reinforcement Learning (RL) has as its main objective to maximize the rewards of an object...