Byrski, Aleksander - ed. ; Kisiel-Dorohinicki, Marek - ed. ; Dobrowolski, Grzegorz - ed.
In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. ; We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 25, number 3 (2015) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Jul 14, 2025
May 2, 2024
123
https://zbc.uz.zgora.pl/repozytorium/publication/88894
| Edition name | Date |
|---|---|
| A strategy learning model for autonomous agents based on classification | Jul 14, 2025 |
Wiśniewska, Joanna Sawerwain, Marek Obuchowicz, Andrzej Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
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