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Reinforcement learning (RL) can be applied to a wide class of problems because it requires no other information than perceived states and rewards to find good action policies. However, it takes a large number of trials before acquiring the optimal policy. In order to make RL faster, use of subgoals is proposed. Since errors and ambiguity are inevitable in(More)
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