Temporal-Difference Learning for Dragonchess

2026-10-01

AI Summary

This study addresses the challenge of effectively updating evaluation heuristics within the complex state space of 3D Dragon Chess. Leveraging a high-performance engine reconstructed in C++, we systematically compare two adaptive learning strategies: evolutionary transfer learning and TD(λ). Through extensive match statistics and significance testing, experimental results demonstrate that both methods significantly outperform baseline agents, with no statistically significant performance difference observed between them. This research confirms the efficacy of adaptive strategies in novel game domains characterized by structural complexity, providing reliable methodological support for AI-driven solutions to high-dimensional games with incomplete information.

Abstract

Our research investigates how two adaptive AI methods, evolutionary transfer learning and TD(lambda), perform in the three-dimensional chess environment Dragonchess. The game challenges players with its unique board structure and computational load, making it an ideal setting to study how adaptive methods can update evaluation heuristics in novel environments. In this work we re-implement the Dragonchess engine, changing it from a PyGame engine to C++. This enables faster gameplay, allowing us to run 10,000 games with confidence intervals and significance tests, rather than a single small tournament. Both adaptive methods outperform all other agents in the round-robin tournament. Our results showed that there is no significant difference in the performance between the evolved and learned evaluations. This research establishes the efficacy of adaptive methods in structurally complex, novel game domains.

Problem

• Dragonchess
• Temporal-Difference Learning
• evaluation heuristics
• adaptive AI
• three-dimensional chess

Innovation

• Temporal-Difference Learning
• Evolutionary Transfer Learning
• Dragonchess
• Adaptive AI
• Engine Optimization