<ul><li>This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov Decision Process (MDP).</li><li>The implementation and optimization of agent-environment interactions are done entirely within the quantum domain, eliminating reliance on classical computations.</li><li>Key contributions include quantum-based state transitions, return calculation, and trajectory search mechanisms that utilize quantum principles to demonstrate the realization of RL processes through quantum phenomena.</li><li>Experimental results show the capacity of a quantum model to achieve quantum advantage in RL, highlighting the potential of fully quantum implementations in decision-making tasks.</li></ul>

Quantum framework for Reinforcement Learning: integrating Markov Decision Process, quantum arithmetic, and trajectory search

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