A layered approach to learning coordination knowledge in multiagent environments

Date

2007-12-01

Author

Erus, Guray
Polat, Faruk

Metadata

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Multiagent learning involves acquisition of cooperative behavior among intelligent agents 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 paper, we propose a new RL technique called the Two Level Reinforcement Learning with Communication (2LRL) method to provide cooperative action selection in a multiagent environment. In 2LRL, learning takes place in two hierarchical levels; in the first level agents learn to select their target and then they 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.

Subject Keywords

Reinforcement learning, Hierarchical reinforcement learning, Multiagent learning

URI

https://hdl.handle.net/11511/40069

Journal

APPLIED INTELLIGENCE

DOI

https://doi.org/10.1007/s10489-006-0034-y

Collections

Department of Computer Engineering, Article

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Citation Formats

G. Erus and F. Polat, “A layered approach to learning coordination knowledge in multiagent environments,” APPLIED INTELLIGENCE, pp. 249–267, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40069.