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One of the problems in reinforcement learning is that as the environment becomes more complex, the number of parameters used in decision making increase which leads us to a slow decision making process. The main idea here is to come up with a new algorithm which is able to transfer the information, using data mining techniques in extracting the patterns. We introduce a new algorithm for state transitions and actions which happen during the transfer by the agent are saved as a data set for data mining techniques which is presented Learning With Action Transfer (LAT). The main idea is to use the repeated action in each state, as a pattern in similar states as a means to improve learning speed and performance. The results in our algorithm will be compared to the results in Qlearning algorithm..

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