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Deep Reinforcement Learning With Quantum-Inspired Experience Replay.

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    A novel quantum-inspired training method for deep reinforcement learning (DRL) adaptively selects experiences, balancing exploration and exploitation. This DRL with quantum-inspired experience replay (DRL-QER) improves training efficiency and outperforms existing algorithms on Atari games.

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    Area of Science:

    • Artificial Intelligence
    • Quantum Computing
    • Machine Learning

    Background:

    • Deep Reinforcement Learning (DRL) relies on experience replay for training.
    • Traditional experience replay can be inefficient in balancing exploration and exploitation.

    Purpose of the Study:

    • To propose a novel training paradigm, DRL with quantum-inspired experience replay (DRL-QER).
    • To enhance DRL by adaptively selecting experiences based on complexity and replay count.

    Main Methods:

    • Formulating transitions in quantum representations.
    • Applying quantum-inspired preparation and depreciation operations on transitions.
    • Using TD-errors and replay count to guide experience selection.

    Main Results:

    • DRL-QER demonstrates superior performance over state-of-the-art algorithms on Atari 2600 games.
    • Improved training efficiency was observed with DRL-QER.
    • The method is compatible with memory-based DRL approaches like double and dueling networks.

    Conclusions:

    • DRL-QER offers an effective approach to optimize experience replay in DRL.
    • The quantum-inspired paradigm enhances both training efficiency and performance.
    • This method provides a promising direction for advancing DRL techniques.