Abstract:In this work authors extend the model of the reinforcement learning paradigm for multi-agent systems called "Influence Value Reinforcement Learning" (IVRL). In previous work an algorithm for repetitive games was proposed, and it outperformed traditional paradigms. Here, authors define an algorithm based on this paradigm for using when agents has to learn from delayed rewards, thus, an influence value reinforcement learning algorithm for two agents stochastic games. The IVRL paradigm is based on social interact… Show more
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