Learning intelligent behavior in a non-stationary and partially observable environment

Date

2002-10-01

Author

Senkul, S
Polat, Faruk

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Individual learning in an environment where more than one agent exist is a challenging task. In this paper, a single learning agent situated in an environment where multiple agents exist is modeled based on reinforcement learning. The environment is non-stationary and partially accessible from an agents' point of view. Therefore, learning activities of an agent is influenced by actions of other cooperative or competitive agents in the environment. A prey-hunter capture game that has the above characteristics is defined and experimented to simulate the learning process of individual agents. Experimental results show that there are no strict rules for reinforcement learning. We suggest two new methods to improve the performance of agents. These methods decrease the number of states while keeping as much state as necessary.

Subject Keywords

Linguistics and Language, Artificial Intelligence, Language and Linguistics

URI

https://hdl.handle.net/11511/39233

Journal

ARTIFICIAL INTELLIGENCE REVIEW

DOI

https://doi.org/10.1023/a:1019935502139

Collections

Department of Computer Engineering, Article

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Citation Formats

S. Senkul and F. Polat, “Learning intelligent behavior in a non-stationary and partially observable environment,” ARTIFICIAL INTELLIGENCE REVIEW, pp. 97–115, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39233.