Date of Award
8-2026
Document Type
Thesis
Degree Name
Bachelor of Arts (BA)
Second Department
Natural Sciences
First Advisor
Gillman, David
Area of Concentration
Computer Science
Abstract
This thesis presents the development of MEIJI, an artificial intelligence (AI) agent that applies temporal difference (TD) learning to play Colonial Diplomacy within a custom reinforcement learning (RL) environment. While reinforcement learning has previously been applied to the board game Diplomacy, no reinforcement learning environment has been developed for the Colonial Diplomacy variant. To address this gap, this thesis implements a complete RL environment that models the rules and mechanics of Colonial Diplomacy and enables TD agents to learn through self-play. The project lays the groundwork for an investigation of whether the trained MEIJI agents learn to recognize the strategic importance of opposing Great Powers during gameplay. This is evaluated by comparing the number of supply centers controlled by an agent with those controlled by each opponent, thereby identifying which Great Power is closest to achieving victory. By analyzing the agents’ learned behavior, this investigation would examine immediate strategic threats in a complex multi-agent environment and contribute a new benchmark for reinforcement learning research in Colonial Diplomacy.
Recommended Citation
Aldama-Apodaca, Alexander, "MEIJI: DEVELOPING A REINFORCEMENT LEARNING AGENT AND ENVIRONMENT FOR COLONIAL DIPLOMACY" (2026). Theses & ETDs. 7015.
https://digitalcommons.ncf.edu/theses_etds/7015
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