Determine Optimal Strategies for Highly Complex TBCA Configurations

Determine the optimal strategy for Turn-Based Combat Arena configurations with sufficiently high state-space complexity, where exhaustive search is impractical.

Background

The paper explains that TBCA can be configured across a wide range of state-space complexities. For simple configurations, an optimal strategy may be found by brute-force search, whereas highly complex configurations require approximation through advanced machine-learning methods.

The authors explicitly state that the optimal strategy remains unknown in the high-complexity regime. The present work develops the framework and balancing methodology but does not solve the optimal-play problem for those configurations.

References

This establishes a connection between simple games with low state-space complexity, where an optimal strategy can be found using brute-force search, and complex games with high state-space complexity, where the optimal strategy remains unknown and can only be approximated using advanced ML methods.

— Turn-Based Combat Arena: A New Framework for Multiagent Training and Game Balancing  (2609.03122 - Vasyuta et al., 2 Sep 2026) in Section 2, State-space complexity