Reinforcement Learning for Stochastic Cooperative Multi-Agent Systems

Authors:
Martin Lauer;Martin Riedmiller
Affiliations:
University of Osnabrück;University of Osnabrück
Venue:
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Year:
2004

Citing 5
Cited 4

The dynamics of reinforcement learning in cooperative multiagent systems

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Distributed Value Functions

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Coordinated Reinforcement Learning

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning

Convergence of independent adaptive learners

EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
A state-cluster based Q-learning

ICNC'09 Proceedings of the 5th international conference on Natural computation
The world of independent learners is not markovian

International Journal of Knowledge-based and Intelligent Engineering Systems
Multi-agent case-based reasoning for cooperative reinforcement learners

ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a distributed variant of Q-learning that allows to learn the optimal cost-to-go function in stochastic cooperative multi-agent domains without communication between the agents.