Reinforcement learning is defined as the problem of an agent that learns to perform a certain task through trial and error interactions with an unknown environment [27]. Most of the research in reinforcement learning focuses on algorithms that are inspired, in a way or another, by methods of Dynamic Programming (e.g., Watkins’ Q-learning [29]). These algorithms have a strong theoretical framework but assume a tabular representation of the value function; thus, their applicability is limited to problems involving few input states and few actions. Alternatively, these methods can be extended for large applications by using function approximators (e.g., neural networks) to represent the value function [27]. In these cases, the general theoretical framework remains but convergence theorems no longer apply.
Learning classifier systems from a reinforcement learning perspective.
LANZI, PIER LUCA
2002-01-01
Abstract
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task through trial and error interactions with an unknown environment [27]. Most of the research in reinforcement learning focuses on algorithms that are inspired, in a way or another, by methods of Dynamic Programming (e.g., Watkins’ Q-learning [29]). These algorithms have a strong theoretical framework but assume a tabular representation of the value function; thus, their applicability is limited to problems involving few input states and few actions. Alternatively, these methods can be extended for large applications by using function approximators (e.g., neural networks) to represent the value function [27]. In these cases, the general theoretical framework remains but convergence theorems no longer apply.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
