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The Effect of Bootstrapping in Multi-Automata
Reinforcement Learning Host Publication: IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning Authors: M. Peeters, A. Nowé and K. Verbeeck Publication Date: Apr. 2007
Abstract: Learning Automata are shown to be an excellent tool for creating learning multi-agent systems. Most algorithms used in current automata research expect the environment to end in an explicit end-stage. In this end-stage the rewards are given to the learning automata (i.e. Monte Carlo updating). This is however unfeasible in sequential decision problems with infinite horizon where no such end-stage exists. In this paper we propose a new algorithm based on one-step returns that uses bootstrapping to find good equilibrium paths in multi-stage games.
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