A Research Framework for Asynchronous Adversarial Multi-Player Games with Human Player GUI and AI Gym
Metadata Field | Value | Language |
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dc.contributor.advisor | Tauritz, Daniel | |
dc.contributor.author | Roberts, Cody | |
dc.date.accessioned | 2023-07-27T19:03:50Z | |
dc.date.available | 2023-07-27T19:03:50Z | |
dc.date.issued | 2023-07-27 | |
dc.identifier.uri | https://etd.auburn.edu//handle/10415/8816 | |
dc.description.abstract | The primary contribution of this thesis is to describe the design and development of a research framework to support complex, real-time asynchronous multi-agent simulations with both homogeneous as well as heterogeneous human and artificial intelligence (AI) agents. This framework provides researchers with a platform to model adversarial games and benchmark AI algorithms and policies. It provides a flexible, reusable client/server architecture that supports a wide variety of games for use as (learning) environments. The secondary contribution of this thesis is to describe the design and development of an example use-case of the research framework for satellite constellations named Satellite Tycoon (Sat-Tycoon). The provided React client provides researchers, educators, and aerospace enthusiasts with a way to play the provided Sat-Tycoon game from their internet browser without the need for downloading or installing. The Sat-Tycoon game itself is a satellite constellation simulation rich with complexity, ideal for use as a reinforcement learning environment. This thesis will cover the design decisions made for these contributions, and the trade-offs involved in those decisions. Such decisions include using multiple networked gyms instead of a single multi-agent gym, using the OpenAI gym standard instead of PettingZoo, building a server authoritative architecture over a peer-to-peer architecture, and using a web framework instead of a game engine. | en_US |
dc.subject | Computer Science and Software Engineering | en_US |
dc.title | A Research Framework for Asynchronous Adversarial Multi-Player Games with Human Player GUI and AI Gym | en_US |
dc.type | Master's Thesis | en_US |
dc.embargo.status | NOT_EMBARGOED | en_US |
dc.embargo.enddate | 2023-07-27 | en_US |
dc.contributor.committee | Davide, Guzzetti | |
dc.contributor.committee | Mulder, Samuel | |
dc.contributor.committee | Rao, Akhil |