Quantum Deep Q-Learning with Distributed Prioritized Experience Replay
Samuel Yen-Chi Chen
Abstract
This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay, asynchronous training and novel matrix loss into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q-learning with the same model architecture. The proposed framework holds potential for more complex tasks while maintaining training efficiency.
Topics & Concepts
Reinforcement learningComputer scienceAsynchronous communicationBaseline (sea)Sampling (signal processing)Artificial intelligenceQuantumDistributed computingMachine learningComputer networkGeologyOceanographyPhysicsComputer visionFilter (signal processing)Quantum mechanicsQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyNeural Networks and Reservoir Computing