@misc{10481/98141,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/98141},
abstract = {In this work, we propose a methodology to implement classic Markov Decision Processes in a Quantum Computing paradigm, as a first step to achieve systems running Quantum Reinforcement Learning where both agent and environment are expressed as quantum programs. To do so, we analyze the interaction cycle between the agent and the environment in classic reinforcement learning and create a method to map a Markov Decision Process with discrete state space, action set, and rewards, into a quantum program.},
organization = {This article was supported by the project QUANERGY (Ref. TED2021-129360B-I00), Ecological and Digital Transition R&D projects call 2022 funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR.},
publisher = {University of Naples Federico II},
keywords = {quantum reinforcement learning},
keywords = {quantum machine learning},
title = {Implementation of Markov Decision Processes into quantum algorithms for reinforcement learning},
author = {Pegalajar Cuéllar, Manuel and Pegalajar Palomino, María del Carmen and Baca Ruiz, Luis Gonzaga and Navarro Garulo, Gabriel and Cano Gutiérrez, Carlos and Servadei, Lorenzo},
}