Learning Multi-agent Strategies in Multi-stage Collaborative Games

Authors:
W. Andy Wright
Affiliations:
-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

Citing 5
Cited 0

Technical Note: \cal Q-Learning

Machine Learning
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
Sequential Strategy for Learning Multi-stage Multi-agent Collaborative Games

ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Fast Planning in Stochastic Games

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
On the convergence of stochastic iterative dynamic programming algorithms

Neural Computation

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Abstract

An alternative approach to learning decision strategies in multi-state multiple agent systems is presented here. The method, which uses a game theoretic construction which is model free and does not rely on direct communication between the agents in the system. Limited experiments show that the method can find Nash equilibrium point for 3 player multi-stage game and converges more quickly than a comparable co-evolution method.