@inproceedings{7bb09ecca4a14063ab83e8b1eb050a3d,
title = "Advances in Measuring Inflation Within Virtual Economies Using Deep Reinforcement Learning",
abstract = "This paper assesses an improved framework for evaluating the performance of economies within online multiplayer games. Games in this medium traditionally require a great deal of testing and analysis to assess the outcomes and affects of player interactions. This process is normally imperfect and time consuming, leading a lot of games developers and publishers to maintain a lot of risk during the development and launch of multi-million dollar projects. The framework and example project presented within this paper uses deep reinforcement learning to simulate economic interactions between learning agents. This is possible by having agents learn from player demonstrations to interact with game systems such as Battling, Collecting Resources and Crafting Weapons which allows them to co-ordinate and cooperate to achieve long-term goals in the game. This paper shows recent breakthroughs in relation to training these agents to the variety of metrics learning agents can generate to help games designers test and polish multiplayer experience, which have proved hard to quantify and measure without extensive play testing. This paper is an extension paper to our publication in ICAART 2021 [21], within this paper includes the following additions: Further discussion on the economic properties of Eve OnlineAn evaluation of how player demonstrations can accelerate the training time for both agent{\textquoteright}s policiesA measured example of how this tool can balance multiplayer game economies through parameter tuning.",
keywords = "Artificial intelligence, Game economies, Games design, Reinforcement learning",
author = "Conor Stephens and Chris Exton",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 13th International Conference on Agents and Artificial Intelligence, ICAART 2021 ; Conference date: 04-02-2021 Through 06-02-2021",
year = "2022",
doi = "10.1007/978-3-031-10161-8_16",
language = "English",
isbn = "9783031101601",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "294--314",
editor = "Rocha, {Ana Paula} and Luc Steels and {van den Herik}, Jaap",
booktitle = "Agents and Artificial Intelligence - 13th International Conference, ICAART 2021, Revised Selected Papers",
}