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It is hard to define a state space or the proper reward function in reinforcement learning to make the robot act as expected. In this paper, we demonstrate the expected behavior for a robot Then a RL-based decision tree approach which decides to split according to long-term evaluations, instead of a top-down greedy strategy which finds out the relationship between the input and output from the demonstration...
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