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Reinforcement learning agents store their knowledge such as state-action value in look-up tables. However, loop-up table requires large memory space when number of states become large. Learning from look-up table is tabularasa therefore is very slow. To overcome this disadvantage, generalization methods are used to abstract knowledge. In this paper, decision tree technology is used to enable the agent...
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