Hierarchical Multiagent Reinforcement Learning in Markov Games
نویسنده
چکیده
Interactions between intelligent agents in multiagent systems can be modeled and analyzed by using game theory. The agents select actions that maximize their utility function so that they also take into account the behavior of the other agents in the system. Each agent should therefore utilize some model of the other agents. In this paper, the focus is on the situation which has a temporal structure and in which the exact form of the interaction between the learning agents is initially unknown and should be learned from the experience.
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