Solving Multi-stage Games with Hierarchical Learning Automata That Bootstrap.

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 absorbing 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 don΄t have to travel all the way up to the top of the hierarchy and in which there is no need for explicit end-stages. [ABSTRACT FROM AUTHOR]