-
Nemecký jazyk
Learning in Cooperative Multi-Agent Systems
Autor: Thomas Gabel
In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally... Viac o knihe
Na objednávku
73.98 €
bežná cena: 82.20 €
O knihe
In a distributed system, a number of individually acting agents coexist. In order to achieve a common goal, coordinated cooperation between the agents is crucial. Many real-world applications are well-suited to be formulated in terms of spatially or functionally distributed entities. Job-shop scheduling represents one such application. Multi-agent reinforcement learning (RL) methods allow for automatically acquiring cooperative policies based solely on a specification of the desired joint behavior of the whole system. However, the decentralization of the control and observation of the system among independent agents has a significant impact on problem complexity. The author Thomas Gabel addresses the intricacy of learning and acting in multi-agent systems by two complementary approaches. He identifies a subclass of general decentralized decision-making problems that features provably reduced complexity. Moreover, he presents various novel model-free multi-agent RL algorithms that are capable of quickly obtaining approximate solutions in the vicinity of the optimum. All algorithms proposed are evaluated in the scope of various established scheduling benchmark problems.
- Vydavateľstvo: Südwestdeutscher Verlag für Hochschulschriften AG Co. KG
- Rok vydania: 2017
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Nemecký jazyk
- ISBN: 9783838110363
Anglický jazyk 
