Add to ORKGAbstract—This paper deals with reinforcement lear ning for process modeling and control using a model-free, action- dependent adaptive critic (ADAC). A new modified recursive Levenberg Marquardt (RLM) training algorithm, called temporal difference RLM, is developed to improve the ADAC performance. Novel application results for a simulated continuously-stirred-tank-reactor process are included to show the superi-ority of the new algorithm to conventional temporal-difference stochastic backpropagation. Index Terms—Action-dependent adaptive critic, intelligent control, mul-tilayer perceptrons, neural networks, nonlinear process control, process op-timization, reinforcement learning
This work addresses three problems with reinforcement learning and adap-tive neuro-control: 1. Non-M...
Abstract Adaptive (or actor) critics are a class of reinforcement learning algorithms. Generally, in...
Abstract. In this paper we present a Reinforcement Learning (RL) ap-proach with the capability to tr...
This paper deals with reinforcement lear ning for process modeling and control using a model-free, ...
This paper deals with reinforcement lear ning for process modeling and control using a model-free, ...
This paper deals with reinforcement lear ning for process modeling and control using a model-free, ...
An intelligent controller has the ability to analyse an unknown situation and to respond to it accor...
An intelligent controller has the ability to analyse an unknown situation and to respond to it accor...
An intelligent controller has the ability to analyse an unknown situation and to respond to it accor...
This paper investigates the feasibility of applying reinforcement learning (RL) concepts to industri...
This paper investigates the feasibility of applying reinforcement learning (RL) concepts to industri...
This paper describes extensions of previous `adaptive critics' which have been onedimensional, ...
Classical control theory requires a model to be derived for a system, before any control design can ...
Meta-learning is a branch of machine learning which trains neural network models to synthesize a wid...
Classical control theory requires a model to be derived for a system, before any control design can ...
This work addresses three problems with reinforcement learning and adap-tive neuro-control: 1. Non-M...
Abstract Adaptive (or actor) critics are a class of reinforcement learning algorithms. Generally, in...
Abstract. In this paper we present a Reinforcement Learning (RL) ap-proach with the capability to tr...
This paper deals with reinforcement lear ning for process modeling and control using a model-free, ...
This paper deals with reinforcement lear ning for process modeling and control using a model-free, ...
This paper deals with reinforcement lear ning for process modeling and control using a model-free, ...
An intelligent controller has the ability to analyse an unknown situation and to respond to it accor...
An intelligent controller has the ability to analyse an unknown situation and to respond to it accor...
An intelligent controller has the ability to analyse an unknown situation and to respond to it accor...
This paper investigates the feasibility of applying reinforcement learning (RL) concepts to industri...
This paper investigates the feasibility of applying reinforcement learning (RL) concepts to industri...
This paper describes extensions of previous `adaptive critics' which have been onedimensional, ...
Classical control theory requires a model to be derived for a system, before any control design can ...
Meta-learning is a branch of machine learning which trains neural network models to synthesize a wid...
Classical control theory requires a model to be derived for a system, before any control design can ...
This work addresses three problems with reinforcement learning and adap-tive neuro-control: 1. Non-M...
Abstract Adaptive (or actor) critics are a class of reinforcement learning algorithms. Generally, in...
Abstract. In this paper we present a Reinforcement Learning (RL) ap-proach with the capability to tr...