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Book Publication

Solving Multi-Stage Games with Hierarchical Learning Automata that Bootstrap

Host Publication: Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning

Authors: M. Peeters, K. Verbeeck and A. Nowé

Publisher: Springer

Publication Date: Feb. 2008

ISBN: 978-3-540-77947-6


Abstract:

Hierarchical learning automata are shown to be an excellent tool for solving multi-stage games. However, most updating schemes used by hierarchical automata expect the multi-stage game to reach an ab- sorbing state at which point the automata are updated in a Monte Carlo way. As such, the approach is infeasible for large multi-stage games (and even for problems with an infinite horizon) and the convergence process is slow. In this paper we propose an algorithm where the rewards do not have to travel all the way up to the top of the hierarchy and in which there is no need for explicit end-stages.

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