Can I read Reinforcement Learning in a Distributed Market-based Production Control System on EtoBox?

Reinforcement Learning in a Distributed Market-based Production Control System by Balázs Csanád Csáji; László Monostori; Botond Kádár is a Engineering article available to read on EtoBox.

What is Reinforcement Learning in a Distributed Market-based Production Control System about?

The paper presents an adaptive iterative distributed scheduling algorithm that operates in a market-based production control system. The manufacturing system is agentified, thus, every machine and job is associated with its own software agent. Each agent learns how to select presumably good schedules, by this way the size of the search space can be reduced. In order to get adaptive behavior and search space reduction, a triple-level learning mechanism is proposed. The top level of learning incorporates a simulated annealing algorithm, the middle (and the most important) level contains a reinforcement learning system, while the bottom level is done by a numerical function approximator, such as an artificial neural network. The paper suggests a cooperation technique for the agents, as well. It also analyzes the time and space complexity of the solution and presents some experimental results.

Who reads Reinforcement Learning in a Distributed Market-based Production Control System?

Author

Balázs Csanád Csáji; László Monostori; Botond Kádár

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 1474-0346)

Published

2006

Language

EN

Field

Engineering (Physical Sciences)