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As a widely used reinforcement learning method, Q-learning is bedeviled by the curse of dimensionality: The computational complexity grows dramatically with the size of state-action space. To combat this difficulty, an integrated hierarchical Q-learning framework is proposed based on the hybrid Markov decision process (MDP) using temporal abstraction instead of the simple MDP. The learning process...
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