11.4.1 — Strategy Parameter Optimization (M11.4) #71
Description
Description
Implement automated strategy parameter tuning using optimization algorithms to find well-balanced strategy configurations. Reduce dominant strategy win rate deltas and improve strategic diversity.
Acceptance Criteria
- Script
scripts/optimize_strategies.pyaccepts strategy parameter ranges and optimization targets - Supports multiple optimization algorithms: grid search, random search, and optionally Bayesian optimization
- Optimization runs batches of sweep simulations and evaluates fitness against targets
- Output includes Pareto frontier of optimal configurations (trade-offs between balance vs. difficulty)
- Integration with result storage (11.2.1) to track optimization runs and outcomes
- Documentation describes optimization workflow, tuning targets, and result interpretation
- At least 10 tests covering optimization algorithms, fitness evaluation, and parameter validation
Priority
Low (Optional enhancement - future work)
Dependencies
- ✅ 11.1.1 (Batch Parameter Sweeps) - Completed
- ✅ 11.2.1 (Result Aggregation & Storage) - Completed
Responsible
gamedev-agent
Future Work
Implementation plan Section 10 describes exposing internal strategy parameters (aggression thresholds, risk tolerance, resource prioritization) for deeper tuning. This task focuses on optimizing existing high-level strategy behavior first.
References
See .pm/tracker.md task 11.4.1 for complete details and context.