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Related Experiment Videos

Decentralized learning in Markov games.

Peter Vrancx¹, Katja Verbeeck, Ann Nowé

¹Computational Modeling Laboratory (COMO), Vrije Universiteit Brussel, 1050 Brussels, Belgium.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|July 18, 2008

Summary

Decentralized learning automata (LA) can now control Markov games, extending their use in multiagent reinforcement learning. This algorithm converges to a pure equilibrium point for agent policies under ergodic assumptions.

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Area of Science:

Artificial Intelligence
Machine Learning
Game Theory

Background:

Learning automata (LA) are effective for multiagent reinforcement learning.
LA theory enables decentralized control of Markov chains with unknown parameters.

Purpose of the Study:

Extend LA control algorithms to Markov games.
Analyze convergence properties in a multiagent setting.

Main Methods:

Adaptation of decentralized independent LA algorithms.
Application to Markov games, an extension of Markov decision problems.
Analysis under ergodic assumptions.

Main Results:

The extended algorithm successfully controls Markov games.
Convergence to a pure equilibrium point is demonstrated.
Validation of LA theory's applicability to more complex scenarios.

Conclusions:

LA provide a robust framework for multiagent reinforcement learning in Markov games.
The proposed extension maintains theoretical guarantees of convergence.
This work expands the utility of LA in distributed decision-making problems.