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A novel learning policy in multi-agent reinforcement learning is presented, trying to find another tradeoff of exploration and exploitation efficiently, It use the output of the classical quantum computer as an input for chaotic dynamics amplifier, The novel amplifier consider the chaotic effect, it can amplify the initial value in polynomial time. It considers the action selection problem and argues...
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