torre-math-engine

Deterministic combat math applies seed-based randomness, clamping, and exact formulas for hit chance, damage, and initiative to reproduce identical outcomes. You implement these modular functions within your combat loop to verify balancing across different seeds and party configurations.

Seed-based randomness ensures simulation reproducibility by generating identical combat results from the same input data. It allows developers to verify engine balancing, test scaling logic, and validate AI decision making across multiple floor configurations consistently.

You validate combat engine balancing by applying deterministic formulas for hit chance, damage, initiative, and XP scaling within a simulation. Using seed data and modular functions, you reproduce exact outcomes to verify formula correctness across party configurations.

Yes, you can reproduce identical combat simulation outcomes by using deterministic inputs and seed-based randomness. Applying exact math formulas for initiative, damage, and bounty logic ensures consistent results regardless of party configuration or floor progression.

Yes, the deterministic math engine provides modular functions for AI decision making and initiative calculations. It applies exact formulas with seed-based randomness and clamping to ensure combat outcomes remain reproducible across different simulation runs and engine states.

Deterministic combat formulas require exact seed data and strict clamping to maintain reproducibility. Any deviation in input seeds or party configurations breaks outcome consistency, meaning simulations cannot produce varied results without explicitly changing the underlying seed values.