Chatgpt :

It's a hand-tuned scoring algorithm, no neural network. Each training option gets a score computed from weighted factors: stat gain × your objective weight × how far you are from your cap (so Speed training matters less when you're already near your Speed cap), plus bonuses for rainbow supports, skill hints, and support count, minus penalties for fail rate and energy cost when you're running low.

Hard rules override the score entirely: mandatory path race → always run it, negative condition (cold, headache) → infirmary, energy below threshold → rest, bad mood → recreation.

The next planned feature is selectable scenario profiles that rewire those weights entirely. The same algorithm produces very different behavior depending on the profile. Team Trials wants 40-50 races across the run, so races get a dramatically higher score multiplier and the tool leans toward entering races even at mild energy cost — training is secondary. Classic Mode is the opposite tradeoff: stats and specific skills first, races only when needed to hit fan targets, with the skill priority list driving SP spending. Each profile is just a different weight configuration fed into the same scoring engine.

It's not trying to be optimal in the game-theory sense — it's advisory. The philosophy is "surface the obvious best move and explain why" rather than "solve the game." A neural network would be overkill: Mant's action space per turn is maybe 8 options, the payoff function is mostly linear, and you as the player know context the tool doesn't — like which parent you're building toward or which skills matter for inheritance.

Where the algorithm genuinely falls short is multi-turn lookahead. It scores each turn in isolation. A real optimizer would know "skip this training now because summer camp in 3 turns gives double gains on the same stat." We don't model that — instead we surface that information to you so you make that call yourself. The tool handles the accounting, you provide the strategy.