Files
AIHostingTycoon/packages/game-engine/src/systems/researchBonuses.ts
T
josh 102e05c8ba
Balance Check / balance-simulation (push) Failing after 11m32s
Balance Check / multi-run-balance (push) Failing after 23m46s
CI / build-and-push (push) Successful in 1m20s
Add game-simulation package with multi-run balance testing, fix stalled-pipeline trap
Adds a full simulation harness (game-simulation package) with greedy/random strategies,
36-metric diagnostics, multi-run orchestration via child processes, and a statistical
interpreter. Includes 2.3x engine performance optimizations (research bonus caching,
per-DC dirty tracking, reduced allocations in tick pipeline, single-pass loops).

Fixes a critical balance bug where training pipelines stalled on insufficient VRAM would
permanently block training slots — the engine never re-checked stalled pipelines, and the
greedy strategy didn't pre-check VRAM requirements. This caused 20-25% of seeds to get
stuck in Scale-up era. All three fixes (engine un-stalling, strategy VRAM pre-check,
stalled pipeline cancellation) bring pass rate from 75% to 100% across 20 random seeds.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-26 06:11:26 -04:00

100 lines
3.2 KiB
TypeScript

import { TECH_TREE } from '../data/techTree';
export interface ResearchBonuses {
energyCostReduction: number;
pipelineSpeedBonus: number;
trainingSpeedBonus: number;
inferenceEfficiencyBonus: number;
tokensPerFlopBonus: number;
dataQualityBonus: number;
sdkCoverageBonus: number;
globalCapabilityBonus: number;
reasoningBonus: number;
codingBonus: number;
creativeBonus: number;
multimodalBonus: number;
agentsBonus: number;
reputationBonus: number;
safetyBonus: number;
autoScalingBonus: number;
}
const techTreeById = new Map(TECH_TREE.map(n => [n.id, n]));
let _cachedBonuses: ResearchBonuses | null = null;
let _cachedResearchCount = -1;
export function getResearchBonuses(completedResearch: string[]): ResearchBonuses {
if (_cachedBonuses && completedResearch.length === _cachedResearchCount) {
return _cachedBonuses;
}
const bonuses: ResearchBonuses = {
energyCostReduction: 0,
pipelineSpeedBonus: 0,
trainingSpeedBonus: 0,
inferenceEfficiencyBonus: 0,
tokensPerFlopBonus: 0,
dataQualityBonus: 0,
sdkCoverageBonus: 0,
globalCapabilityBonus: 0,
reasoningBonus: 0,
codingBonus: 0,
creativeBonus: 0,
multimodalBonus: 0,
agentsBonus: 0,
reputationBonus: 0,
safetyBonus: 0,
autoScalingBonus: 0,
};
for (const id of completedResearch) {
const node = techTreeById.get(id);
if (!node) continue;
for (const effect of node.effects) {
switch (effect.type) {
case 'efficiency_boost':
switch (effect.target) {
case 'training_speed': bonuses.trainingSpeedBonus += effect.value; break;
case 'inference': bonuses.inferenceEfficiencyBonus += effect.value; break;
case 'tokens_per_flop': bonuses.tokensPerFlopBonus += effect.value; break;
case 'pipeline_speed': bonuses.pipelineSpeedBonus += effect.value; break;
case 'data_quality': bonuses.dataQualityBonus += effect.value; break;
case 'sdk_coverage': bonuses.sdkCoverageBonus += effect.value; break;
case 'auto_scaling': bonuses.autoScalingBonus += effect.value; break;
}
break;
case 'capability_boost':
switch (effect.target) {
case 'all': bonuses.globalCapabilityBonus += effect.value; break;
case 'reasoning': bonuses.reasoningBonus += effect.value; break;
case 'coding': bonuses.codingBonus += effect.value; break;
case 'creative': bonuses.creativeBonus += effect.value; break;
case 'multimodal': bonuses.multimodalBonus += effect.value; break;
case 'agents': bonuses.agentsBonus += effect.value; break;
case 'reputation': bonuses.reputationBonus += effect.value; break;
}
break;
case 'cost_reduction':
if (effect.target === 'energy') bonuses.energyCostReduction += effect.value;
break;
case 'safety_boost':
bonuses.safetyBonus += effect.value;
break;
}
}
}
_cachedBonuses = bonuses;
_cachedResearchCount = completedResearch.length;
return bonuses;
}
export function resetResearchBonusCache(): void {
_cachedBonuses = null;
_cachedResearchCount = -1;
}