Abstract
A major 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 become more and more complex and learning slows down. The presence of possibly redundant information is one of the causes of this issue. Current 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 space, which is exponential in the number of agents. A solution to this problem lies in the use of Generalized Learning Automata (GLA). In this chapter we will first demonstrate how GLA can help take the correct actions in large unknown environments. Secondly, we introduce a general framework for multi-agent learning, where learning happens on two separate layers and agents learn when to observe each other. Within this framework we introduce a new algorithm, called 2observe, which uses a GLA-approach to distinguish between high risk states where the agents have to take each others presence into account and low risk states where they can act independently. Finally we apply this algorithm to a gridworld problem because of the similarities to some real-world problems, such as autonomous robot control.
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De Hauwere, YM., Vrancx, P., Nowé, A. (2010). Multi-Agent Systems and Large State Spaces. In: Hãkansson, A., Hartung, R., Nguyen, N.T. (eds) Agent and Multi-agent Technology for Internet and Enterprise Systems. Studies in Computational Intelligence, vol 289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13526-2_9
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DOI: https://doi.org/10.1007/978-3-642-13526-2_9
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