A strategy learning model for autonomous agents based on classification - Śnieżyński, Bartłomiej (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: autonomous agents; strategy learning; supervised learning; classification; reinforcement learning