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Multi-layer learning and knowledge transfer in MAS Host Publication: the 7th European Workshop on Multi-Agent Systems Authors: Y. De Hauwere, P. Vrancx and A. Nowé Publication Date: Dec. 2009 Number of Pages: 13
Abstract: A ma jor challenge in multi-agent reinforcement learning remains dealing with the large state spaces typically associated with realistic multi-agent systems. As the state space grows, agent policies be-
come more and more complex and learning slows down. Current more advanced single-agent techniques are already very capable of learning optimal policies in large unknown environments. When multiple agents are present however, we are challenged by an increase of the state-action space, exponential in the number of agents, even though these agents do not always interfere with each other and thus their presence should not always be included in the state information of the other agent. We introduce a framework capable of dealing with this issue. We also present an implementation of our framework, called 2observe which we apply to some gridworld problems. Furthermore we demonstrate that our approach is capable of transferring its knowledge to new agents entering the environment.
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