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cleanup on spelling
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eidesstattliche.md

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## Eidesstattliche Erklärung
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Hiermit versichere ich an Eides statt, dass ich die vorliegende Arbeit selbst- ständig und ohne die Benutzung anderer
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als der angegebenen Hilfsmittel angefertigt habe. Alle Stellen, die wörtlich oder sinngemäß aus veröffentlichten und
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nicht veröffentlichten Schriften entnommen wurden, sind als solche kenntlich gemacht. Die Arbeit ist in gleicher oder
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ähnlicher Form oder auszugsweise im Rahmen einer anderen Prüfung noch nicht vorgelegt worden. Ich versichere, dass die
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eingereichte elektronische Fassung der eingereichten Druckfassung vollständig entspricht.
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src/acronyms.tex

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%:.,+33sort
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\acro {AI} {Artificial Intelligence}
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\acro {CHP} {Combined Heat and Power Unit}
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\acro {CLI} {Command Line Interface}
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\acro {ReLu} {Rectified Linear Unit}
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\acro {CPU} {Central Processing Unit}
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\acro {mWh} {megawatt hour}
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\acro {LSTM} {Long-Short Term Memory}
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\acro {RNN} {Recurrent Neural Network}
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\acro {SL} {Supervised Learning}
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\acro {SSL} {Secure Socket Layers}
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\acro {UL} {Unsupervised Learning}
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\acro {UI} {User Interface}
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\acro {VM} {Virtual Machine}
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\end {acronym}

src/body.tex

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src/main.tex

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\input{cover.tex}
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\pagenumbering{Roman}
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\input{abstract.tex}
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\input{preface.tex}
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%\printacronyms
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\listoffigures
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\listoftables

src/preface.tex

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\chapter{Preface}
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This thesis was planned and discussed in the winter of 17/18. On February 1st, the work phase of six months started.
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Within these six months, I discovered many previously unknown or unforeseen complexities. These include the
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communication technologies developed to permit a complete python based broker and a large variety of API approaches
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within the RL agent libraries currently available. While I have invested a significant amount of effort into the
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development of the required components, I always intended to build something that may be reused in the future instead of
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being discarded after my thesis was graded. This lead me to the decision of implementing a best practice based
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communication instead of a quick minimal approach and led me to try and write my python code in a way that will let
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future broker developers reuse it as a framework for their broker implementations.
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As of July, I was not able to complete my research question and reach the intended target of evaluating a variety of
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neural network architectures that let a RL learn from other agents in its environment. Because of university
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regulations, changing a thesis title is not permitted. And while my research question was not answered, I believe I have
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contributed something valuable for the PowerTAC community. With my implementation, current state-of-the-art neural
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network algorithms and especially reinforcement agent implementations can be used to act in the PowerTAC competition.
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While I was not able to complete this in time and offer valubale, testable results, it is nonetheless now possible to
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work on a broker and to focus on the core problems of RL learning problems: Environment observation filtering, NN input
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preprocessing, reward function definition, NN architecture experimentation etc. With the created Docker images,
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developers are quickly able to start a competition with multiple brokers and future participants may be encouraged to
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adopt the Docker based distribution of their agents to include more advanced technologies in their broker
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implementations without placing a burden on others to manage these dependencies.
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When reading the thesis, please be aware that the title does not match the contents as one would expect. If I had more
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time to work on this project, by the time I handed in my thesis I was at the point where I could have started developing
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and experimenting with a number of RL agent implementations and to make the project complete. Unfortunately, I fell
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into the same trap that many software engineers and entire project teams fall into: Underestimating the complexity of
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the project which leads to either loss in quality, time overruns or budget overruns. I recognize this mistake but I
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cannot fix it today. I hope the thesis is still valuable to anyone who reads it and maybe the next graduate theses will
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continue where I left off.

thesis.vim

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ab RL \ac{RL}
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ab CLI \ac{CLI}
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ab UI \ac{UI}
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ab SSL \ac{SSL}
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ab JSON \ac{JSON}
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ab ReLu \ac{ReLu}
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ab GRPC \ac{GRPC}

todos.md

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- "walk backwards" from bandit to continuous action space
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- try with more input types / preprocess better
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- draw.io graphic on wholesale components
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- clean up WholesaleObservationSpace vs simply passing the environment (text l 1944 )
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