Quantum Reinforcement Learning

Abstract

A novel quantum reinforcement learning is proposed through combining quantum theory and reinforcement learning. Inspired by state superposition principle, a framework of state value update algorithm is introduced. The state/action value is represented with quantum state and the probability of action eigenvalue is denoted by probability amplitude, which is updated according to rewards. This approach makes a good tradeoff between exploration and exploitation using probability and can speed up learning. The results of simulated experiment verified its effectiveness and superiority.