@article{Doe:2020,
    author = "Doe, John",
    title = "Jak citovat",
    subtitle = "Citace článku",
    journal = "Seriál o tvorbě prací",
    journalsubtitle = "Formální náležitosti",
    howpublished = "online",
    address = "Brno",
    publisher = "Fakulta informačních technologií VUT v Brně",
    contributory = "Přeložil Jan NOVÁK",
    edition = "1",
    version = "verze 1.0",
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    year = "2020",
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    note = "Toto je zcela vymyšlená citace",
    url = "https://merlin.fit.vutbr.cz"
}

@book{sutton2018reinforcement,
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}

@book{rao2022foundations,
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}

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}

@misc{finrl-portfolio-allocation-2020,
    doi = {10.48550/ARXIV.2111.03995},
    url = {https://arxiv.org/abs/2111.03995},
    author = {Guan, Mao and Liu, Xiao-Yang},
    keywords = {Portfolio Management (q-fin.PM), Artificial Intelligence (cs.AI), FOS: Economics and business, FOS: Economics and business, FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach},
    publisher = {arXiv},
    year = {2021},
    copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{sirucek-2015,
    author = {Širůček, Martin and Křen, Lukáš},
    journal = {Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis},
    month = {9},
    number = {4},
    pages = {1375--1386},
    publisher = {Mendel University Press},
    title = {{Application of Markowitz Portfolio Theory by Building Optimal Portfolio on the US Stock Market}},
    volume = {63},
    year = {2015},
    doi = {10.11118/actaun201563041375},
    url = {http://dx.doi.org/10.11118/actaun201563041375},
}

@misc{Model-Free-Reinforcement-Learning-for-Asset-Allocation,
    doi = {10.48550/ARXIV.2209.10458},
    url = {https://arxiv.org/abs/2209.10458},
    author = {Oshingbesan, Adebayo and Ajiboye, Eniola and Kamashazi, Peruth and Mbaka, Timothy},
    keywords = {Portfolio Management (q-fin.PM), Machine Learning (cs.LG), FOS: Economics and business, FOS: Economics and business, FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Model-Free Reinforcement Learning for Asset Allocation},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

@misc{rl-course-david-silver,
    author = {David Silver},
    title = {Introduction to Reinforcement Learning with David Silver},
    year = {2015},
    url = {https://www.deepmind.com/learning-resources/introduction-to-reinforcement-learning-with-david-silver},
    howpublished = "online",
    cited = "2023-04-08",
}

@misc{Pyfolio,
    author = {Quantopian Inc.},
    title = {Pyfolio},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/quantopian/pyfolio}},
    commit = {4b901f6d73aa02ceb6d04b7d83502e5c6f2e81aa}
}

@misc{Pyfolio-return-analysis,
    author = {Sravz},
    title = {Pyfolio - Return Analysis},
    howpublished = "online",
    url = {https://docs.sravz.com/docs/analytics/pyfolio-returns-analysis/},
    cited = "2023-04-08",
}

@misc{investopedia,
    author = {Investopedia},
    title = {Investopedia},
    url = {https://www.investopedia.com},
    howpublished = "online",
    cited = "2023-04-16",
}

@misc{kurniawati2021partially,
    title = {Partially Observable Markov Decision Processes (POMDPs) and Robotics},
    author = {Hanna Kurniawati},
    year = {2021},
    eprint = {2107.07599},
    archivePrefix = {arXiv},
    primaryClass = {cs.RO}
}

@misc{finta,
    author = {peerchemist},
    title = {Finta},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/peerchemist/finta}},
    commit = {af01fa594995de78f5ada5c336e61cd87c46b151},
    cited = {2023-04-16},
}

@misc{liu2022finrlmeta,
    title = {FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning},
    author = {Xiao-Yang Liu and Ziyi Xia and Jingyang Rui and Jiechao Gao and Hongyang Yang and Ming Zhu and Christina Dan Wang and Zhaoran Wang and Jian Guo},
    year = {2022},
    eprint = {2211.03107},
    archivePrefix = {arXiv},
    primaryClass = {q-fin.TR}
}

@mastersthesis{FITMT25127,
    author = "David Vosol",
    type = "Master's thesis",
    title = "Aplikace posilovan\'{e}ho u\v{c}en\'{i} v \v{r}\'{i}zen\'{i} autonomn\'{i}ho vozidla",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2022,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/25127/"
}

@misc{enwiki:1148510872,
    author = "{Wikipedia contributors}",
    title = "Stochastic process --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2023",
    url = "https://en.wikipedia.org/w/index.php?title=Stochastic_process&oldid=1148510872",
    note = "[Online; accessed 10-April-2023]"
}

@article{ABATE2021102207,
    title = {Adaptive formal approximations of Markov chains},
    journal = {Performance Evaluation},
    volume = {148},
    pages = {102207},
    year = {2021},
    issn = {0166-5316},
    doi = {https://doi.org/10.1016/j.peva.2021.102207},
    url = {https://www.sciencedirect.com/science/article/pii/S0166531621000249},
    author = {Alessandro Abate and Roman Andriushchenko and Milan Češka and Marta Kwiatkowska},
    keywords = {Markov models, Probabilistic model checking, Approximation techniques, Adaptive aggregation},
    abstract = {We explore formal approximation techniques for Markov chains based on state–space reduction that aim at improving the scalability of the analysis, while providing formal bounds on the approximation error. We first present a comprehensive survey of existing state-reduction techniques based on clustering or truncation. Then, we extend existing frameworks for aggregation-based analysis of Markov chains by allowing them to handle chains with an arbitrary structure of the underlying state space – including continuous-time models – and improve upon existing bounds on the approximation error. Finally, we introduce a new hybrid scheme that utilises both aggregation and truncation of the state space and provides the best available approach for approximating continuous-time models. We conclude with a broad and detailed comparative evaluation of existing and new approximation techniques and investigate how different methods handle various Markov models. The results also show that the introduced hybrid scheme significantly outperforms existing approaches and provides a speedup of the analysis up to a factor of 30 with the corresponding approximation error bounded within 0.1%.}
}

@ARTICLE{7110602,
    author = {Liu, Derong and Wei, Qinglai and Yan, Pengfei},
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    year = {2015},
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    number = {12},
    pages = {1577-1591},
    doi = {10.1109/TSMC.2015.2417510} }
}

@inproceedings{inproceedings,
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    year = {2012},
    month = {12},
    pages = {},
    title = {An Introduction to Markov Chains},
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}

@inproceedings{perotto:hal-02363599,
    title = {Tuning the Discount Factor in Order to Reach Average Optimality on Deterministic MDPs},
    author = {Perotto, Filipo Studzinski and Vercouter, Laurent},
    url = {https://hal.science/hal-02363599},
    booktitle = {{International Conference on Innovative Techniques and Applications of Artificial Intelligence}},
    address = {Cambridge, United Kingdom},
    year = {2018},
    doi = {10.1007/978-3-030-04191-5\_7},
    keywords = {Average Dynamic Programming ; Sensitive Dynamic Programming ; Reinforcement Learning ; Markovian Decision Process},
    pdf = {https://hal.science/hal-02363599/file/2018___SGAI__CR____Tuning_the_discount_for_average_optimal_D_MDPs.pdf},
    hal_id = {hal-02363599},
    hal_version = {v1},
}

@misc{adamczyk2023bounding,
    title = {Bounding the Optimal Value Function in Compositional Reinforcement Learning},
    author = {Jacob Adamczyk and Volodymyr Makarenko and Argenis Arriojas and Stas Tiomkin and Rahul V. Kulkarni},
    year = {2023},
    eprint = {2303.02557},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}


@misc{enwiki:1043516653,
    author = "{Wikipedia contributors}",
    title = "Modern portfolio theory --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2021",
    url = "https://en.wikipedia.org/w/index.php?title=Modern_portfolio_theory&oldid=1043516653",
    note = "[Online; accessed 16-April-2023]"
}

@misc{enwiki:1141766585,
    author = "{Wikipedia contributors}",
    title = "Dow Jones Industrial Average --- {Wikipedia}{,} The Free Encyclopedia",
    year = "2023",
    url = "https://en.wikipedia.org/w/index.php?title=Dow_Jones_Industrial_Average&oldid=1141766585",
    note = "[Online; accessed 16-April-2023]"
}
