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The original Q-learning method is difficult on achieving sample efficiency such as training a policy to get to a goal with in limited time step. So, the Dyna-Q agent is proposed to speed up the policy learning. However, the Dyna-Q did not specify how to build the model, so the table is used to be the model largely. In this paper, we proposed an adaptive model learning method based on tree structures...
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