Search Results - subject:"Multiagent learning"

Model-free nonstationary reinforcement learning : near-optimal regret and applications in multiagent reinforcement learning and inventory control

Mao, Weichao; Zhang, Kaiqing; Zhu, Ruihao; Simchi-Levi, … - In: Management science : journal of the Institute for … 71 (2025) 2, pp. 1564-1580

Persistent link: https://www.econbiz.de/10015411178

Game of Thronesdfully distributed learning for multiplayer bandits

Bistritz, Ilai; Leshem, Amir - In: Mathematics of operations research 46 (2021) 1, pp. 159-178

Persistent link: https://www.econbiz.de/10012498107

Reinforcement learning and its application to Othello

Wezel, M.C. van; Eck, N.J.P. van - Erasmus University Rotterdam, Econometric Institute - 2005

] Michael Bowling and Manuela Veloso. Multiagent learning using a variable learning rate. Arti cial Intelligence, 136 … management science from the literature. Key words: Arti cial Intelligence, Reinforcement Learning, Q-learning, Multiagent … Learning, Markov Decision Processes, Dynamic Programming, Neural Networks, Game Playing, Gaming, Othello. 1 Introduction Many …

Persistent link: https://www.econbiz.de/10005450895

Reinforcement learning and its application to Othello

van Wezel, van Wezel, M.C.; van Eck, van Eck, N.J.P. - Faculteit der Economische Wetenschappen, Erasmus … - 2005

In this article we describe reinforcement learning, a machine learning technique for solving sequential decision problems. We describe how reinforcement learning can be combined with function approximation to get approximate solutions for problems with very large state spaces. One such problem...

Persistent link: https://www.econbiz.de/10010837883

Multiagent Learning within a collaborative environment

STANCIU, Cristina Ofelia; COJOCARIU, Adrian; KAZI, Ljubica - In: Anale. Seria Stiinte Economice. Timisoara XVIII (2012) May, pp. 185-188

Multiagent Learning is at the intersection of multiagent systems and Machine Learning, two subdomains of artificial …

Persistent link: https://www.econbiz.de/10010641495

MULTIAGENT LEARNING FOR BLACK BOX SYSTEM REWARD FUNCTIONS

TUMER, KAGAN; AGOGINO, ADRIAN - In: Advances in Complex Systems (ACS) 12 (2009) 04, pp. 475-492

In large, distributed systems composed of adaptive and interactive components (agents), ensuring the coordination among the agents so that the system achieves certain performance objectives is a challenging proposition. The key difficulty to overcome in such systems is one of credit assignment:...

Persistent link: https://www.econbiz.de/10008512510

LEARNING FROM ACTIONS NOT TAKEN IN MULTIAGENT SYSTEMS

TUMER, KAGAN; KHANI, NEWSHA - In: Advances in Complex Systems (ACS) 12 (2009) 04, pp. 455-473

needs to account for the actions of other learning agents. In this paper, we present a multiagent learning approach that …

Persistent link: https://www.econbiz.de/10008512511

LEARNING INDIRECT ACTIONS IN COMPLEX DOMAINS: ACTION SUGGESTIONS FOR AIR TRAFFIC CONTROL

AGOGINO, ADRIAN; TUMER, KAGAN - In: Advances in Complex Systems (ACS) 12 (2009) 04, pp. 493-512

Providing intelligent algorithms to manage the ever-increasing flow of air traffic is critical to the efficiency and economic viability of air transportation systems. Yet, current automated solutions leave existing human controllers "out of the loop" rendering the potential solutions both...

Persistent link: https://www.econbiz.de/10008512513