<ul><li>This paper introduces a quantum framework for reinforcement learning (RL) tasks, incorporating quantum principles and utilizing a fully quantum model of the classical Markov Decision Process (MDP).</li><li>The framework employs quantum concepts and a quantum search algorithm to implement and optimize agent-environment interactions within the quantum domain, eliminating the need for classical computations.</li><li>Key contributions include quantum-based state transitions, return calculation, and trajectory search mechanisms that leverage quantum principles to demonstrate RL processes through quantum phenomena.</li><li>The study showcases the role of quantum superposition in improving computational efficiency for RL tasks, indicating the potential of fully quantum models in decision-making processes.</li></ul>

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

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