The Effect of Bootstrapping in Multi-Automata Reinforcement Learning

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