2LRL: a two-level multi-agent reinforcement learning algorithm with communication

Erus, Guray
Learning is a key element of an "intelligent" computational system. In Multi- agent Systems (MASs), learning involves acquisition of a cooperative behavior in order to satisfy the joint goals. Reinforcement Learning (RL) is a promising unsupervised machine learning technique inspired from the earlier studies in animal learning. In this thesis, we propose the Two Level Reinforcement Learning with Communication (2LRL) method, a new RL technique to provide cooperative action selection in a multi-agent environment. In 2LRL, the decision mechanism of the agents is divided into two hierarchical levels, in which the agents learn to select their target in the first level and to select the action directed to their target in the second level. The agents communicate their perception to their neighbors and use the communication information in their decision-making. We applied 2LRL method in a hunter-prey environment and observed a satisfactory cooperative behavior.


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Citation Formats
G. Erus, “2LRL: a two-level multi-agent reinforcement learning algorithm with communication,” M.S. - Master of Science, Middle East Technical University, 2002.