Learning Intelligent Behavior in a Non-stationary and Partially Observable Environment

Artificial Intelligence Review - Tập 18 - Trang 97-115 - 2002
SelÇuk şenkul1, Faruk Polat1
1Computer Engineering Department, Middle East Technical University, Ankara, Turkey

Tóm tắt

Individual learning in an environment where more than one agent exist is a chal-lengingtask. In this paper, a single learning agent situated in an environment where multipleagents exist is modeled based on reinforcement learning. The environment is non-stationaryand partially accessible from an agents' point of view. Therefore, learning activities of anagent 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 experimentedto simulate the learning process of individual agents. Experimental results show that thereare no strict rules for reinforcement learning. We suggest two new methods to improve theperformance of agents. These methods decrease the number of states while keeping as muchstate as necessary.

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Artificial Intelligence Review

Tập 18

97-115

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