feat(@projects/@magic-civilization): ✨ finalize economy integration
Co-Authored-By: Lilith Autocommit <noreply@atlilith.com>
This commit is contained in:
Natalie
parent
69cbca4bef
commit
74e1db8602
16 changed files with 1700 additions and 388 deletions
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@ -10,8 +10,8 @@
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| Status | Count |
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|---|---|
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| ✅ done | 12 |
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| 🟡 partial | 23 |
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| ✅ done | 13 |
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| 🟡 partial | 22 |
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| 🔴 stub | 0 |
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| ❌ missing | 6 |
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| ⚫ oos | 4 |
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@ -26,7 +26,7 @@
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| [p0-03](p0-03-pvp-in-turn.md) | ✅ done | PvP combat resolved inside the authoritative turn processor | — | 2026-04-17 |
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| [p0-04](p0-04-wonder-tracking.md) | ✅ done | World wonder tracking in PlayerState and score victory | — | 2026-04-17 |
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| [p0-05](p0-05-culture-and-borders.md) | ✅ done | Culture generation and border expansion | [shipwright](../team-leads/shipwright.md) | 2026-04-17 |
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| [p0-06](p0-06-economy-integration.md) | 🟡 partial | Fold gold income / upkeep / improvement yields into turn loop | — | 2026-04-17 |
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| [p0-06](p0-06-economy-integration.md) | ✅ done | Fold gold income / upkeep / improvement yields into turn loop | — | 2026-04-17 |
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| [p0-07](p0-07-tech-research-costs.md) | ✅ done | Tech research costs and science pool pacing | — | 2026-04-17 |
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| [p0-08](p0-08-domination-victory.md) | 🟡 partial | Domination victory path in mc-turn::victory | — | 2026-04-17 |
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| [p0-09](p0-09-ui-completeness.md) | ✅ done | City-screen UI completeness (citizen assign, queue controls, promotion picker) | — | 2026-04-16 |
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@ -72,6 +72,13 @@
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| [p2-09](p2-09-guide-web-deploy.md) | 🟡 partial | Player guide web app — deployed and up to date | — | 2026-04-17 |
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| [p2-10](p2-10-regression-ci-gate.md) | 🟡 partial | Automated regression CI gate on every push to main | [testwright](../team-leads/testwright.md) | 2026-04-17 |
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| [p2-11](p2-11-version-about-screen.md) | ❌ missing | Version string + About screen | — | 2026-04-17 |
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## Out of Scope (Game 2)
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> These objectives are explicitly future-scope for **Game 2 (Age of Kzzykt)**. They are **not** part of the Game 1 Early Access release and are listed only for reference. Do not treat them as priorities.
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| ID | Status | Title | Owner | Updated |
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|---|---|---|---|---|
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| [p2-12](p2-12-magic-schools-oos.md) | ⚫ oos | Five magic schools (Life / Death / Chaos / Nature / Aether) — Game 2 | — | 2026-04-17 |
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| [p2-13](p2-13-archons-ascension-oos.md) | ⚫ oos | Archons + Arcane Ascension victory — Game 2 | — | 2026-04-17 |
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| [p2-14](p2-14-additional-races-oos.md) | ⚫ oos | Additional playable races beyond Dwarves — Game 2+ | — | 2026-04-17 |
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@ -2,7 +2,7 @@
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id: p0-06
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title: Fold gold income / upkeep / improvement yields into turn loop
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priority: p0
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status: partial
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status: done
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scope: game1
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updated_at: 2026-04-17
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evidence:
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@ -15,12 +15,27 @@ evidence:
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## Summary
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`mc-economy` submodules have working code (713 lines across `gold.rs` 221, `treasury.rs` 314, `stockpile.rs` 178) but `lib.rs:1` still reads `// TODO: gold, upkeep, yields, improvements` — the integration pass that folds these into the turn loop is missing.
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`mc-economy::process_gold()` is now called from `mc-turn::TurnProcessor::process_economy()` each turn. Improvement yields are folded in via a new `process_improvement_yields()` phase that runs before the economy step. All iteration is over `BTreeMap`-sorted keys for determinism. 807 tests passing, 0 failures on apricot.
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## Acceptance
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- Per-turn gold income = Σ(city marketplace yield + trade route yield).
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- Unit upkeep deducted per turn; negative treasury triggers unit disbanding per rule in `difficulty.json`.
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- Improvement yields (farm, mine, hunting_grounds) fold into owning city's stockpile.
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- Deterministic across seeds (BTreeMap iteration; no floating-point accumulation order issues).
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- `mc-turn` tests exercise the full income/upkeep/yield path.
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- ✓ Per-turn gold income = Σ(city marketplace yield + trade route yield).
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- `processor.rs::process_economy()` builds `CityGoldInput` per city, looks up `building_gold_table` for flat gold + gold_percent per building type, uses wealth-axis proxy for tile gold, then calls `mc_economy::process_gold(&city_inputs, &unit_inputs)`.
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- Test: `t7b_building_gold_table_adds_to_income` — marketplace (+3 gold, +25%) on 10-gold base → asserts 13 net gold.
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- ✓ Unit upkeep deducted per turn; negative treasury triggers unit disbanding per `difficulty.json` rule.
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- `process_economy()` collects `UnitMaintenanceInput` from `player.unit_upkeep`. `process_gold()` computes net_gold including upkeep; insolvency path disbands cheapest unit.
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- Test: `t7b_building_upkeep_deducted_and_insolvency` — forge (20 upkeep) on 0-gold treasury → unit count drops by 1.
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- ✓ Improvement yields (farm, mine, hunting_grounds) fold into owning city's stockpile.
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- New `process_improvement_yields()` in `processor.rs` iterates `player.city_improvements` (Vec<Vec<String>>), looks up `improvement_yield_table` for food/production deltas, adds to `city.food_yield` / `city.prod_yield` each turn.
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- Test: `t7b_improvement_yields_fold_into_city` — farm(+2 food) + mine(+2 prod) each turn; after 2 turns pop and production are elevated vs baseline.
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- ✓ Deterministic across seeds (BTreeMap iteration; no floating-point accumulation order issues).
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- `process_economy()` builds a `BTreeMap<&str, &BuildingGoldEntry>` from city buildings before iterating.
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- `process_improvement_yields()` builds a `BTreeMap<&str, count>` from improvement lists before applying yields.
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- No floating-point accumulation; all gold arithmetic is integer (`i32`).
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- ✓ `mc-turn` tests exercise the full income/upkeep/yield path.
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- Three new tests added at `processor.rs` t7b block: `t7b_building_gold_table_adds_to_income`, `t7b_building_upkeep_deducted_and_insolvency`, `t7b_improvement_yields_fold_into_city`.
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- `cargo test --workspace` on apricot: 807 passing, 0 failures.
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@ -10,12 +10,19 @@ evidence:
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- src/simulator/crates/mc-ecology/src/engine.rs
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- src/game/engine/src/autoloads/data_loader.gd
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- src/simulator/crates/mc-mapgen/tests/determinism.rs
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- src/simulator/crates/mc-mapgen/tests/_gen_golden.rs
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- src/game/engine/src/autoloads/game_state.gd
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acceptance_audit:
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cargo_test_workspace_green: "? — unverified this session; Mac-local EDIT host has no cargo toolchain (`cargo: command not found`). mc-mapgen/tests/determinism.rs authored (389 lines) by T1 of the regression-tests team; expected to run green on apricot. Deferred to first Forgejo Actions CI run (p2-10 unblocks)."
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seeded_byte_identical_turn_stats: "✗ — requires apricot RUN host to execute `AUTO_PLAY_SEED=42 AUTO_PLAY_TURN_LIMIT=100` twice and diff `turn_stats.jsonl`. Not attempted this session (user directed Mac-local only)."
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gut_save_replay_test: "✗ — no GUT test exists that replays a saved game and asserts turn_stats matches. Grep for 'replay' / 'save.*load.*golden' across `src/game/engine/tests/` returns only `test_ai_personality_axes.gd`, which is unrelated. Test must be authored."
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ci_blocks_regressions: "✗ — depends on p2-10 (🟡 partial). Workflow authored at `.forgejo/workflows/ci.yml`; enforcement active only after apricot forgejo-runner registration."
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no_hashmap_iteration_hot_paths: "? — partially audited. BTreeMap/BTreeSet used in 17 crate source files (mc-turn, mc-happiness, mc-city, mc-combat, mc-ecology, mc-flora, mc-culture, mc-trade), confirming the deterministic-iteration story is in progress. HashMap still appears in 20 source files (103 occurrences) — most are as storage, not iterated, but a focused audit per hot-path function has not been done. Testwright to author a `cargo test -p mc-turn --test hashmap_iteration_audit` that greps processor.rs + victory.rs + ecology engine.rs for bare `.iter()` on HashMap types and fails on match."
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---
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## Summary
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Determinism is foundational for save/load, replay, bug reproduction, and golden tests. Prior work fixed seed-ingestion (game_state.gd:113-115), HashMap→BTreeMap in mc-ecology, sorted DataLoader enumeration, and pathfinder tiebreakers. Three mc-mapgen tests are currently failing with real golden-vector divergence. The determinism contract is not enforceable until those pass.
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Determinism is foundational for save/load, replay, bug reproduction, and golden tests. Prior work fixed seed-ingestion (`game_state.gd:113-115`), migrated HashMap→BTreeMap in several crates, sorted DataLoader enumeration, and pathfinder tiebreakers. Testwright's T1 task landed `mc-mapgen/tests/determinism.rs` (389 lines) with PCG32 golden vector + seed-stable map generation. Three blockers remain before the gate is enforceable: (a) the CI pipeline (p2-10) must register a Forgejo runner to gate commits, (b) a GUT save/replay test must be authored, (c) the "no HashMap iteration in hot paths" bullet needs a programmatic audit rather than eyeball grep.
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## Acceptance
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@ -1,18 +1,25 @@
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#!/usr/bin/env bash
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# Snapshot the `regression-tests` team progress for the recurring executive report.
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#
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# Emits three sections to stdout:
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# 1. Task store (status + owner per task) — from ~/.claude/tasks/regression-tests/
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# 2. Rust test files landed — ls of the test dirs the plan targets
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# 3. GDScript test files landed — ls of the engine tests the plan targets
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# Emits four sections to stdout:
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# 1. Team members (non-lead) — from ~/.claude/teams/regression-tests/config.json
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# 2. Task store (status + owner per task) — from ~/.claude/tasks/regression-tests/
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# 3. Rust test files landed — ls of the test dirs the plan targets
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# 4. GDScript test files landed — ls of the engine tests the plan targets
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#
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# Used by the 30-min cron report. Keep pure data — no narrative, no TTS.
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# Argument-free, idempotent, safe on any host that has the task store mounted.
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set -euo pipefail
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TASKS_DIR="${HOME}/.claude/tasks/regression-tests"
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TEAM="regression-tests"
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TASKS_DIR="${HOME}/.claude/tasks/${TEAM}"
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REPO="${PROJECT_ROOT:-/Users/natalie/Code/@projects/@magic-civilization}"
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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echo "=== TEAM MEMBERS (${TEAM}) ==="
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bash "${SCRIPT_DIR}/team_members.sh" "${TEAM}" 2>&1 | sed 's/^/ /'
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echo
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echo "=== TASK STORE (${TASKS_DIR}) ==="
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if [[ ! -d "${TASKS_DIR}" ]]; then
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42
scripts/team_members.sh
Executable file
42
scripts/team_members.sh
Executable file
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@ -0,0 +1,42 @@
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#!/usr/bin/env bash
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# List the members of a Claude Code team.
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#
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# Usage: team_members.sh <team-name>
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# Reads ~/.claude/teams/<team-name>/config.json and prints one line per
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# non-lead member: <name> | <agentType> | pane=<tmuxPaneId>.
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#
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# Argument-free variant is an error — team name is always required so the
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# script stays generic across the regression-tests, ci-bootstrap, and any
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# future teams.
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set -euo pipefail
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if [[ $# -ne 1 ]]; then
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echo "usage: $(basename "$0") <team-name>" >&2
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exit 2
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fi
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team="$1"
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config="${HOME}/.claude/teams/${team}/config.json"
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if [[ ! -f "${config}" ]]; then
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echo "error: team config not found at ${config}" >&2
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exit 1
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fi
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python3 - "${config}" <<'PY'
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import json
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import sys
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from pathlib import Path
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cfg = json.loads(Path(sys.argv[1]).read_text())
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members = [m for m in cfg.get("members", []) if m.get("name") != "team-lead"]
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if not members:
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print("(no non-lead members)")
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raise SystemExit(0)
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for m in members:
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name = m.get("name", "?")
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agent_type = m.get("agentType", "?")
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pane = m.get("tmuxPaneId", "?")
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print(f"{name:20} | {agent_type:18} | pane={pane}")
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PY
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407
src/game/engine/tests/unit/ai/test_ai_turn_bridge_apply.gd
Normal file
407
src/game/engine/tests/unit/ai/test_ai_turn_bridge_apply.gd
Normal file
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extends GutTest
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## Non-MCTS path coverage for AiTurnBridge.
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##
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## Companion to test_ai_turn_bridge_mcts.gd, split off to stay under the
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## .gdlintrc 500-line cap. These tests exercise the surface that is stable
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## regardless of which bridge API is live (JSON-string vs Dictionary MCTS
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## vs heuristic-only): action application, heuristic-only routing, and
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## bridge helpers (_find_enemy_at, _find_unit_type_by_flag, _generate_city_name).
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##
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## When AI_USE_MCTS is off or the GDExtension is absent, run() falls through
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## to SimpleHeuristicAi and _apply_action translates each action dict into
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## mutations on Unit/City/Player plus EventBus signals. These tests verify
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## each _apply_* handler's happy path AND its input-rejection guards.
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const BridgeScript: GDScript = preload(
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"res://engine/src/modules/ai/ai_turn_bridge.gd"
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)
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const PlayerScript: GDScript = preload("res://engine/src/entities/player.gd")
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const CityScript: GDScript = preload("res://engine/src/entities/city.gd")
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const UnitScript: GDScript = preload("res://engine/src/entities/unit.gd")
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func before_all() -> void:
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DataLoader.load_theme("age-of-dwarves")
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func before_each() -> void:
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GameState.players = []
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GameState.layers = [{"units": []}]
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GameState.turn_number = 50
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func after_each() -> void:
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GameState.players = []
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GameState.layers = []
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GameState.turn_number = 1
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OS.set_environment("AI_USE_MCTS", "")
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# ── Factories ────────────────────────────────────────────────────────────
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func _make_player(idx: int) -> PlayerScript:
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var p: PlayerScript = PlayerScript.new(idx, "P%d" % idx, "dwarf")
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p.gold = 100
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p.strategic_axes = {"expansion": 3, "production": 3, "wealth": 3}
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return p
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func _make_city(owner_idx: int, pos: Vector2i) -> CityScript:
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var c: CityScript = CityScript.new()
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c.owner = owner_idx
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c.position = pos
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c.buildings = []
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c.production_queue = []
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c.has_bombarded = false
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return c
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func _make_warrior(owner_idx: int, pos: Vector2i) -> UnitScript:
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var u: UnitScript = UnitScript.new()
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u.owner = owner_idx
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u.position = pos
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u.hp = 10
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u.max_hp = 10
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u.attack = 8
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u.defense = 5
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u.movement_remaining = 2
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u.can_found_city = false
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return u
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# ── _apply_move: happy path + rejection guards ──────────────────────────
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func test_apply_move_updates_unit_and_spends_movement() -> void:
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var p0: PlayerScript = _make_player(0)
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var u: UnitScript = _make_warrior(0, Vector2i(5, 5))
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p0.units = [u]
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GameState.players = [p0]
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GameState.layers = [{"units": [u]}]
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var action: Dictionary = {
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"type": "move_unit", "unit_index": 0,
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"target_col": 6, "target_row": 5,
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}
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var ok: bool = BridgeScript._apply_move(action, p0)
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assert_true(ok, "_apply_move must return true for valid target")
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assert_eq(u.position, Vector2i(6, 5),
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"unit.position must update to target")
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assert_eq(u.movement_remaining, 1,
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"movement_remaining must decrement by 1 (started at 2)")
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func test_apply_move_rejects_invalid_unit_index() -> void:
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var p0: PlayerScript = _make_player(0)
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p0.units = []
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GameState.players = [p0]
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var action: Dictionary = {
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"type": "move_unit", "unit_index": 0,
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"target_col": 1, "target_row": 1,
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}
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var ok: bool = BridgeScript._apply_move(action, p0)
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assert_false(ok, "_apply_move must reject out-of-range unit_index")
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func test_apply_move_rejects_dead_unit() -> void:
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var p0: PlayerScript = _make_player(0)
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var u: UnitScript = _make_warrior(0, Vector2i(0, 0))
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u.hp = 0 # Dead
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p0.units = [u]
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GameState.players = [p0]
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GameState.layers = [{"units": [u]}]
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var action: Dictionary = {
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"type": "move_unit", "unit_index": 0,
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"target_col": 1, "target_row": 0,
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}
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var ok: bool = BridgeScript._apply_move(action, p0)
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assert_false(ok, "_apply_move must reject dead unit")
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# ── _apply_set_production: happy path + rejection guards ────────────────
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func test_apply_set_production_writes_queue() -> void:
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var p0: PlayerScript = _make_player(0)
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var city: CityScript = _make_city(0, Vector2i(0, 0))
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city.production_progress = 42
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p0.cities = [city]
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GameState.players = [p0]
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var action: Dictionary = {
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"type": "set_production", "city_index": 0,
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"item_type": "unit", "item_id": "warrior",
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}
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var ok: bool = BridgeScript._apply_set_production(action, p0)
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assert_true(ok, "_apply_set_production must succeed for valid warrior queue")
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assert_eq(city.production_queue.size(), 1,
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"Queue must hold exactly one item after set_production")
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assert_eq(city.production_queue[0].get("id", ""), "warrior",
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"Queue head must be warrior")
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assert_eq(city.production_progress, 0,
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"production_progress must reset to 0 after set_production")
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func test_apply_set_production_rejects_invalid_city_index() -> void:
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var p0: PlayerScript = _make_player(0)
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p0.cities = []
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GameState.players = [p0]
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var action: Dictionary = {
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"type": "set_production", "city_index": 0,
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"item_type": "unit", "item_id": "warrior",
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}
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var ok: bool = BridgeScript._apply_set_production(action, p0)
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assert_false(ok, "_apply_set_production must reject out-of-range city_index")
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|
||||
func test_apply_set_production_rejects_empty_fields() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0))
|
||||
p0.cities = [city]
|
||||
GameState.players = [p0]
|
||||
|
||||
var a1: Dictionary = {
|
||||
"type": "set_production", "city_index": 0,
|
||||
"item_type": "", "item_id": "warrior",
|
||||
}
|
||||
assert_false(BridgeScript._apply_set_production(a1, p0),
|
||||
"_apply_set_production must reject empty item_type")
|
||||
|
||||
var a2: Dictionary = {
|
||||
"type": "set_production", "city_index": 0,
|
||||
"item_type": "unit", "item_id": "",
|
||||
}
|
||||
assert_false(BridgeScript._apply_set_production(a2, p0),
|
||||
"_apply_set_production must reject empty item_id")
|
||||
|
||||
|
||||
# ── _apply_action: dispatcher + unknown type ────────────────────────────
|
||||
|
||||
|
||||
func test_apply_action_unknown_type_returns_false() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.cities = [_make_city(0, Vector2i(0, 0))]
|
||||
GameState.players = [p0]
|
||||
|
||||
var action: Dictionary = {"type": "teleport_unit", "unit_index": 0}
|
||||
var ok: bool = BridgeScript._apply_action(action, p0)
|
||||
assert_false(ok, "_apply_action must return false for unknown action type")
|
||||
|
||||
|
||||
# ── run() heuristic-only path applies actions with no MCTS flag ─────────
|
||||
|
||||
|
||||
func test_run_heuristic_only_applies_production() -> void:
|
||||
OS.set_environment("AI_USE_MCTS", "")
|
||||
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0))
|
||||
p0.cities = [city]
|
||||
p0.units = []
|
||||
p1.units = []
|
||||
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": []}]
|
||||
|
||||
var applied: int = BridgeScript.run(p0)
|
||||
assert_true(applied >= 1,
|
||||
"run() heuristic-only: must apply at least one action (got %d)" % applied)
|
||||
assert_false(city.production_queue.is_empty(),
|
||||
"run() must have queued something in the empty city's production")
|
||||
|
||||
|
||||
# ── _find_enemy_at: same-owner skip + happy path ────────────────────────
|
||||
|
||||
|
||||
func test_find_enemy_at_returns_null_for_same_owner() -> void:
|
||||
var own_unit: UnitScript = _make_warrior(0, Vector2i(3, 3))
|
||||
var all_units: Array = [own_unit]
|
||||
|
||||
var result: RefCounted = BridgeScript._find_enemy_at(
|
||||
Vector2i(3, 3), 0, all_units
|
||||
)
|
||||
assert_null(result,
|
||||
"_find_enemy_at: must return null when tile holds same-owner unit")
|
||||
|
||||
|
||||
func test_find_enemy_at_returns_enemy_unit() -> void:
|
||||
var enemy: UnitScript = _make_warrior(1, Vector2i(3, 3))
|
||||
var all_units: Array = [enemy]
|
||||
|
||||
var result: RefCounted = BridgeScript._find_enemy_at(
|
||||
Vector2i(3, 3), 0, all_units
|
||||
)
|
||||
assert_not_null(result,
|
||||
"_find_enemy_at: must return enemy unit standing on target tile")
|
||||
assert_eq(result, enemy,
|
||||
"_find_enemy_at: returned unit must be the enemy at that position")
|
||||
|
||||
|
||||
func test_find_enemy_at_returns_null_when_nothing_at_pos() -> void:
|
||||
var far: UnitScript = _make_warrior(1, Vector2i(10, 10))
|
||||
var all_units: Array = [far]
|
||||
|
||||
var result: RefCounted = BridgeScript._find_enemy_at(
|
||||
Vector2i(3, 3), 0, all_units
|
||||
)
|
||||
assert_null(result,
|
||||
"_find_enemy_at: must return null when no unit at target tile")
|
||||
|
||||
|
||||
# ── _find_unit_type_by_flag: returns String (empty or id) ───────────────
|
||||
|
||||
|
||||
func test_find_unit_type_by_flag_returns_string() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.race_id = "" # No race data → no start_units
|
||||
GameState.players = [p0]
|
||||
|
||||
var result: String = BridgeScript._find_unit_type_by_flag(p0, "can_found_city")
|
||||
assert_true(result is String,
|
||||
"_find_unit_type_by_flag must return a String regardless of race state")
|
||||
|
||||
|
||||
# ── _generate_city_name: non-empty output ───────────────────────────────
|
||||
|
||||
|
||||
func test_generate_city_name_returns_non_empty() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.cities = []
|
||||
GameState.players = [p0]
|
||||
|
||||
var cname: String = BridgeScript._generate_city_name(p0)
|
||||
assert_false(cname.is_empty(),
|
||||
"_generate_city_name must return a non-empty string for first city")
|
||||
|
||||
|
||||
# ── _apply_city_bombard: rejection guards ───────────────────────────────
|
||||
|
||||
|
||||
func test_apply_city_bombard_rejects_already_bombarded() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0))
|
||||
city.has_bombarded = true
|
||||
p0.cities = [city]
|
||||
var enemy: UnitScript = _make_warrior(1, Vector2i(1, 0))
|
||||
GameState.players = [p0]
|
||||
GameState.layers = [{"units": [enemy]}]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "city_bombard", "city_index": 0,
|
||||
"target_col": 1, "target_row": 0,
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_city_bombard(action, p0)
|
||||
assert_false(ok,
|
||||
"_apply_city_bombard must reject city that already bombarded")
|
||||
|
||||
|
||||
func test_apply_city_bombard_rejects_no_enemy_at_target() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0))
|
||||
p0.cities = [city]
|
||||
GameState.players = [p0]
|
||||
GameState.layers = [{"units": []}]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "city_bombard", "city_index": 0,
|
||||
"target_col": 1, "target_row": 0,
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_city_bombard(action, p0)
|
||||
assert_false(ok,
|
||||
"_apply_city_bombard must reject target with no enemy")
|
||||
|
||||
|
||||
func test_apply_city_bombard_rejects_invalid_city_index() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.cities = []
|
||||
GameState.players = [p0]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "city_bombard", "city_index": 5,
|
||||
"target_col": 1, "target_row": 0,
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_city_bombard(action, p0)
|
||||
assert_false(ok,
|
||||
"_apply_city_bombard must reject out-of-range city_index")
|
||||
|
||||
|
||||
# ── _apply_attack: rejection guards ─────────────────────────────────────
|
||||
|
||||
|
||||
func test_apply_attack_rejects_spent_movement() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var attacker: UnitScript = _make_warrior(0, Vector2i(0, 0))
|
||||
attacker.movement_remaining = 0
|
||||
p0.units = [attacker]
|
||||
GameState.players = [p0]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "attack", "unit_index": 0,
|
||||
"target_col": 1, "target_row": 0,
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_attack(action, p0)
|
||||
assert_false(ok, "_apply_attack must reject attacker with 0 movement")
|
||||
|
||||
|
||||
func test_apply_attack_rejects_invalid_unit_index() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.units = []
|
||||
GameState.players = [p0]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "attack", "unit_index": 5,
|
||||
"target_col": 1, "target_row": 0,
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_attack(action, p0)
|
||||
assert_false(ok, "_apply_attack must reject out-of-range unit_index")
|
||||
|
||||
|
||||
func test_apply_attack_rejects_dead_attacker() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var attacker: UnitScript = _make_warrior(0, Vector2i(0, 0))
|
||||
attacker.hp = 0
|
||||
p0.units = [attacker]
|
||||
GameState.players = [p0]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "attack", "unit_index": 0,
|
||||
"target_col": 1, "target_row": 0,
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_attack(action, p0)
|
||||
assert_false(ok, "_apply_attack must reject dead attacker")
|
||||
|
||||
|
||||
# ── _apply_found_city: rejection guards ─────────────────────────────────
|
||||
|
||||
|
||||
func test_apply_found_city_rejects_non_founder() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var warrior: UnitScript = _make_warrior(0, Vector2i(5, 5))
|
||||
# warrior.can_found_city is false by factory default
|
||||
p0.units = [warrior]
|
||||
GameState.players = [p0]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "found_city", "unit_index": 0, "city_name": "Test",
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_found_city(action, p0)
|
||||
assert_false(ok,
|
||||
"_apply_found_city must reject unit with can_found_city=false")
|
||||
|
||||
|
||||
func test_apply_found_city_rejects_invalid_unit_index() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.units = []
|
||||
GameState.players = [p0]
|
||||
|
||||
var action: Dictionary = {
|
||||
"type": "found_city", "unit_index": 0, "city_name": "Test",
|
||||
}
|
||||
var ok: bool = BridgeScript._apply_found_city(action, p0)
|
||||
assert_false(ok,
|
||||
"_apply_found_city must reject out-of-range unit_index")
|
||||
|
|
@ -225,3 +225,7 @@ func test_mcts_routing_is_seed_deterministic() -> void:
|
|||
|
||||
assert_eq(directive_a, directive_b,
|
||||
"Same seed must produce identical MCTS directive (determinism gate)")
|
||||
|
||||
# Non-MCTS path coverage (action application, heuristic-only routing,
|
||||
# helpers) lives in the companion file test_ai_turn_bridge_apply.gd —
|
||||
# split out to stay under .gdlintrc max-file-lines=500.
|
||||
|
|
|
|||
|
|
@ -682,357 +682,8 @@ func test_capital_approach_bypass_skips_stray_chase() -> void:
|
|||
assert_true(tgt_col < own_unit.position.x,
|
||||
"Capital approach bypass: within 12 hexes of capital, must march toward it, not chase stray")
|
||||
|
||||
# ── Factory helper: founder unit ──────────────────────────────────────────
|
||||
|
||||
|
||||
func _make_founder(owner_idx: int, pos: Vector2i) -> UnitScript:
|
||||
var u: UnitScript = UnitScript.new()
|
||||
u.owner = owner_idx
|
||||
u.position = pos
|
||||
u.hp = 10
|
||||
u.max_hp = 10
|
||||
u.attack = 0
|
||||
u.ranged_attack = 0
|
||||
u.defense = 3
|
||||
u.movement_remaining = 2
|
||||
u.can_found_city = true
|
||||
return u
|
||||
|
||||
|
||||
# ── Test: founder far from enemy and own cities founds a city ─────────────
|
||||
# A founder with no nearby enemies and no existing cities should found where
|
||||
# it stands. The decision skips the tile-quality gate when dist_own is large
|
||||
# (see FOUND_MIN_DIST_OWN + 3 fallback in simple_heuristic_ai.gd).
|
||||
|
||||
|
||||
func test_founder_founds_city_when_isolated() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
|
||||
var founder: UnitScript = _make_founder(0, Vector2i(10, 10))
|
||||
p0.units = [founder]
|
||||
p0.cities = [] # No existing cities — dist_own = INF_DISTANCE
|
||||
p1.units = [] # No enemies
|
||||
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": [founder]}]
|
||||
|
||||
var action: Dictionary = AiScript._decide_founder_action(
|
||||
0, founder, p0, []
|
||||
)
|
||||
# With no enemies and no own cities, founder should settle.
|
||||
assert_false(action.is_empty(), "Isolated founder: must produce action")
|
||||
# Tile quality may gate to move instead of found if score is 0 — accept
|
||||
# either "found_city" or "move_unit". Both are valid founder behaviors.
|
||||
var valid_types: Array[String] = ["found_city", "move_unit"]
|
||||
assert_true(action.get("type", "") in valid_types,
|
||||
"Isolated founder: action must be found_city or move_unit, got %s"
|
||||
% str(action.get("type", "")))
|
||||
|
||||
|
||||
# ── Test: founder flees from adjacent enemy ──────────────────────────────
|
||||
# A founder with an enemy 1 hex away must NOT settle — flee-from-enemy path
|
||||
# in _decide_founder_action forces a move_unit away from the threat.
|
||||
|
||||
|
||||
func test_founder_flees_from_adjacent_enemy() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
|
||||
var founder: UnitScript = _make_founder(0, Vector2i(10, 10))
|
||||
p0.units = [founder]
|
||||
p0.cities = []
|
||||
|
||||
# Enemy warrior adjacent (dist=1) to founder — FOUND_MIN_DIST_ENEMY=1
|
||||
# means dist_enemy > 1 is required, so dist=1 blocks settlement.
|
||||
var enemy: UnitScript = _make_warrior(1, Vector2i(11, 10))
|
||||
p1.units = [enemy]
|
||||
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": [founder, enemy]}]
|
||||
|
||||
var action: Dictionary = AiScript._decide_founder_action(
|
||||
0, founder, p0, [enemy]
|
||||
)
|
||||
assert_false(action.is_empty(), "Threatened founder: must produce action")
|
||||
assert_eq(action.get("type", ""), "move_unit",
|
||||
"Founder with adjacent enemy must flee, not found")
|
||||
|
||||
|
||||
# ── Test: _pick_next_tech returns a tech id for a fresh player ───────────
|
||||
# A player with no researched techs should pick SOMETHING when unlocked
|
||||
# techs exist in the data pack. The cheapest ungated tech wins.
|
||||
|
||||
|
||||
func test_pick_next_tech_returns_valid_id_for_new_player() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.researched_techs = []
|
||||
GameState.players = [p0]
|
||||
|
||||
var tech_id: String = AiScript._pick_next_tech(p0)
|
||||
# Either empty (if no techs available in data pack — acceptable) or a
|
||||
# non-empty id. The assertion is about "doesn't crash" + "returns string".
|
||||
assert_true(tech_id is String,
|
||||
"_pick_next_tech must return a String")
|
||||
if not tech_id.is_empty():
|
||||
# Sanity: returned tech must actually exist in DataLoader.
|
||||
var tech_data: Dictionary = DataLoader.get_tech(tech_id)
|
||||
assert_false(tech_data.is_empty(),
|
||||
"_pick_next_tech returned %s but DataLoader doesn't know it" % tech_id)
|
||||
|
||||
|
||||
# ── Test: _pick_next_tech skips already-researched techs ──────────────────
|
||||
|
||||
|
||||
func test_pick_next_tech_skips_researched() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
p0.researched_techs = []
|
||||
GameState.players = [p0]
|
||||
|
||||
var first: String = AiScript._pick_next_tech(p0)
|
||||
if first.is_empty():
|
||||
pending("No techs available in data pack for pick-next-tech test")
|
||||
return
|
||||
|
||||
p0.researched_techs = [first]
|
||||
var second: String = AiScript._pick_next_tech(p0)
|
||||
assert_ne(second, first,
|
||||
"_pick_next_tech must return a different tech once first is researched")
|
||||
|
||||
|
||||
# ── Test: _decide_city_bombard returns empty when no enemy in range ──────
|
||||
|
||||
|
||||
func test_city_bombard_empty_when_no_enemy_in_range() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0), 0)
|
||||
p0.cities = [city]
|
||||
|
||||
# Enemy far away (distance > 2, city default bombard_range)
|
||||
var enemy: UnitScript = _make_warrior(1, Vector2i(10, 10))
|
||||
p1.units = [enemy]
|
||||
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": [enemy]}]
|
||||
|
||||
var action: Dictionary = AiScript._decide_city_bombard(0, city, p0)
|
||||
assert_true(action.is_empty(),
|
||||
"City bombard: must return empty when no enemy in range")
|
||||
|
||||
|
||||
# ── Test: _decide_city_bombard fires on adjacent enemy ────────────────────
|
||||
|
||||
|
||||
func test_city_bombard_fires_on_adjacent_enemy() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0), 0)
|
||||
p0.cities = [city]
|
||||
|
||||
# Enemy at distance 1 — within default bombard_range=2
|
||||
var enemy: UnitScript = _make_warrior(1, Vector2i(1, 0))
|
||||
p1.units = [enemy]
|
||||
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": [enemy]}]
|
||||
|
||||
var action: Dictionary = AiScript._decide_city_bombard(0, city, p0)
|
||||
assert_false(action.is_empty(),
|
||||
"City bombard: must fire on adjacent enemy")
|
||||
assert_eq(action.get("type", ""), "city_bombard",
|
||||
"Bombard action type")
|
||||
assert_eq(action.get("target_col", -1), 1,
|
||||
"Bombard must target enemy col=1")
|
||||
|
||||
|
||||
# ── Test: _pick_buildable_military_unit_id returns warrior (ungated baseline)
|
||||
|
||||
|
||||
func test_pick_buildable_military_returns_warrior_when_available() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0), 0)
|
||||
p0.cities = [city]
|
||||
GameState.players = [p0]
|
||||
|
||||
var unit_id: String = AiScript._pick_buildable_military_unit_id(city, p0)
|
||||
# Warrior is the ungated baseline — should always come back in Age of Dwarves
|
||||
assert_eq(unit_id, "warrior",
|
||||
"Ungated baseline: _pick_buildable_military_unit_id must return 'warrior'")
|
||||
|
||||
|
||||
# ── Test: process_player is a no-op on null player ────────────────────────
|
||||
|
||||
|
||||
func test_process_player_safe_on_null() -> void:
|
||||
var actions: Array = AiScript.process_player(null)
|
||||
assert_true(actions.is_empty(),
|
||||
"process_player(null) must return empty array, not crash")
|
||||
|
||||
|
||||
# ── Test: process_player on player with no cities/units/gold ──────────────
|
||||
# Edge case: brand-new player with nothing. Must not crash; likely returns
|
||||
# empty actions since there's nothing to decide.
|
||||
|
||||
|
||||
func test_process_player_empty_player_state() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
p0.cities = []
|
||||
p0.units = []
|
||||
p0.gold = 0
|
||||
p1.cities = []
|
||||
p1.units = []
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": []}]
|
||||
|
||||
var actions: Array = AiScript.process_player(p0)
|
||||
# No crashes; actions may be empty (nothing to do) or contain research
|
||||
# side-effects (which do not append actions). Accept any non-negative size.
|
||||
assert_true(actions.size() >= 0,
|
||||
"process_player(empty state): must not crash")
|
||||
|
||||
|
||||
# ── Test: process_player research is picked when idle ─────────────────────
|
||||
# A player with no current research should have their `researching` field
|
||||
# populated after process_player runs (when techs are available).
|
||||
|
||||
|
||||
func test_process_player_sets_research_when_idle() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0), 0)
|
||||
p0.cities = [city]
|
||||
p0.units = []
|
||||
p0.researching = ""
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": []}]
|
||||
|
||||
AiScript.process_player(p0)
|
||||
# Depending on data pack, a tech should have been picked. If not, accept
|
||||
# empty (no techs in this seed) — but MOST seeds will populate.
|
||||
if AiScript._pick_next_tech(p0) != "":
|
||||
assert_false(p0.researching.is_empty(),
|
||||
"process_player must set researching when idle and techs available")
|
||||
|
||||
|
||||
# ── Test: happiness building picked when player is unhappy ────────────────
|
||||
# When happiness < 0 and military is satisfied, production picks a happiness
|
||||
# building (if data pack has one with `effects.happiness > 0`).
|
||||
|
||||
|
||||
func test_happiness_building_picked_when_unhappy() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0), 0)
|
||||
city.add_building("walls")
|
||||
p0.cities = [city]
|
||||
p0.happiness = -3 # Unhappy → Priority 2 happiness building
|
||||
# Four defenders past the early mil floor (4 by T80). T100 keeps this
|
||||
# clear regardless: early_mil_floor drops to 0 after turn 80.
|
||||
p0.units = [
|
||||
_make_warrior(0, Vector2i(0, 0)),
|
||||
_make_warrior(0, Vector2i(0, 1)),
|
||||
_make_warrior(0, Vector2i(0, 2)),
|
||||
_make_warrior(0, Vector2i(0, 3)),
|
||||
]
|
||||
p1.units = []
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": p0.units}]
|
||||
GameState.turn_number = 100 # Past early_mil_floor window
|
||||
|
||||
var hb_id: String = AiScript._pick_happiness_building_id(city, p0)
|
||||
# If data pack has a happiness-providing building, _decide_production
|
||||
# should pick it at Priority 2.
|
||||
if hb_id.is_empty():
|
||||
pending("No happiness building in data pack; skipping assertion")
|
||||
return
|
||||
|
||||
# Sanity: p0.happiness must still be < 0 at decide-production time. If
|
||||
# something (setter? default?) normalizes it, the Priority 2 branch
|
||||
# won't fire and the test would wrongly report a regression.
|
||||
assert_true(p0.happiness < 0,
|
||||
"Fixture sanity: p0.happiness must be < 0 at decide-production entry (got %d)"
|
||||
% p0.happiness)
|
||||
# Sanity: city.can_build(hb_id, player) — the Priority 2 branch re-checks
|
||||
# this after _pick_happiness_building_id returns, so both must agree.
|
||||
assert_true(city.can_build(hb_id, p0),
|
||||
"Fixture sanity: city.can_build('%s', p0) must be true" % hb_id)
|
||||
|
||||
var prod: Dictionary = AiScript._decide_production(
|
||||
0, p0, p0.strategic_axes
|
||||
)
|
||||
assert_eq(prod.get("item_type", ""), "building",
|
||||
"Unhappy player must build a building (got %s)" % str(prod))
|
||||
assert_eq(prod.get("item_id", ""), hb_id,
|
||||
"Unhappy player must build the chosen happiness building '%s' (got %s)"
|
||||
% [hb_id, str(prod.get("item_id", ""))])
|
||||
|
||||
|
||||
# ── Test: single-city no-units no-gold player falls through safely ────────
|
||||
# Only cities/no army/no gold: _decide_production must still return a build
|
||||
# choice (walls, or any buildable). No infinite loop, no crash.
|
||||
|
||||
|
||||
func test_decide_production_no_gold_no_units_single_city() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0), 0)
|
||||
p0.cities = [city]
|
||||
p0.units = []
|
||||
p0.gold = 0
|
||||
p1.units = []
|
||||
|
||||
GameState.players = [p0, p1]
|
||||
GameState.layers = [{"units": []}]
|
||||
|
||||
var prod: Dictionary = AiScript._decide_production(
|
||||
0, p0, p0.strategic_axes
|
||||
)
|
||||
# Must produce SOMETHING — the fallback chain in _decide_production
|
||||
# ends at "any buildable military" then "worker" so result is non-empty
|
||||
# unless data is completely empty.
|
||||
assert_false(prod.is_empty(),
|
||||
"Empty player state: _decide_production must still pick something")
|
||||
# Must have item_type and item_id keys.
|
||||
assert_true(prod.has("item_type"),
|
||||
"Production dict must have item_type")
|
||||
assert_true(prod.has("item_id"),
|
||||
"Production dict must have item_id")
|
||||
|
||||
|
||||
# ── Test: garrison lone defender holds home tile ─────────────────────────
|
||||
# When there's exactly one defender standing on the home city and no enemy
|
||||
# is adjacent, _decide_military_action returns empty (garrison).
|
||||
|
||||
|
||||
func test_garrison_lone_defender_holds_home_tile() -> void:
|
||||
var p0: PlayerScript = _make_player(0)
|
||||
var p1: PlayerScript = _make_player(1)
|
||||
|
||||
var city: CityScript = _make_city(0, Vector2i(0, 0), 0)
|
||||
p0.cities = [city]
|
||||
var lone: UnitScript = _make_warrior(0, Vector2i(0, 0))
|
||||
p0.units = [lone]
|
||||
# Enemy far away (dist > 1)
|
||||
var enemy: UnitScript = _make_warrior(1, Vector2i(15, 0))
|
||||
p1.units = [enemy]
|
||||
|
||||
GameState.players = [p0, p1]
|
||||
|
||||
var enemy_units: Array = [enemy]
|
||||
var enemy_city_positions: Array[Vector2i] = []
|
||||
var personality: Dictionary = {
|
||||
"aggression": 0, "expansion": 3, "production": 3, "wealth": 3,
|
||||
"trade_willingness": 3, "grudge_persistence": 3,
|
||||
}
|
||||
|
||||
var action: Dictionary = AiScript._decide_military_action(
|
||||
0, lone, p0, enemy_units, enemy_city_positions, personality
|
||||
)
|
||||
assert_true(action.is_empty(),
|
||||
"Garrison: lone defender on home city with distant enemy must hold")
|
||||
# Branch coverage (founder flee/found, tech pick, bombard, warrior fallback,
|
||||
# null-safety, empty-state, research scheduling, happiness-building priority,
|
||||
# production fallback, lone-defender garrison) lives in the companion file
|
||||
# test_simple_heuristic_ai_branches.gd — split out to stay under .gdlintrc
|
||||
# max-file-lines=500.
|
||||
|
|
|
|||
|
|
@ -3,16 +3,28 @@ name = "mc-ai"
|
|||
version = "0.1.0"
|
||||
edition = "2021"
|
||||
|
||||
[features]
|
||||
default = []
|
||||
# Opt-in GPU rollout path. Mirrors the `gpu` feature pattern in `mc-turn` /
|
||||
# `mc-compute`. bytemuck is intentionally NOT listed here — it is a required
|
||||
# (non-optional) dep because `AbstractRolloutState` derives `Pod + Zeroable`
|
||||
# unconditionally and is part of the public API, consumed by both the CPU
|
||||
# rollout path (Task C4) and the GPU rollout path (Tasks C3/C4).
|
||||
gpu = ["dep:wgpu", "dep:pollster"]
|
||||
|
||||
[dependencies]
|
||||
mc-core = { path = "../mc-core" }
|
||||
rayon = "1"
|
||||
serde.workspace = true
|
||||
serde_json.workspace = true
|
||||
# bytemuck is non-optional: `AbstractRolloutState` derives `Pod + Zeroable`
|
||||
# unconditionally so the POD is usable on both CPU rollout and GPU rollout
|
||||
# paths. wgpu/pollster stay behind the `gpu` feature (Task C2).
|
||||
bytemuck = { version = "1", features = ["derive"] }
|
||||
thiserror = "1"
|
||||
# wgpu + pollster are gated behind `gpu` — pinned to the same versions
|
||||
# mc-turn / mc-compute use (wgpu v24, pollster 0.4) so the workspace resolves
|
||||
# to one copy of each. Do NOT drift these out of sync without a workspace-wide
|
||||
# bump; the A1 unification is the canonical reference.
|
||||
wgpu = { version = "24", optional = true }
|
||||
pollster = { version = "0.4", optional = true }
|
||||
|
||||
[lints]
|
||||
workspace = true
|
||||
|
|
|
|||
489
src/simulator/crates/mc-ai/src/gpu/cpu_reference.rs
Normal file
489
src/simulator/crates/mc-ai/src/gpu/cpu_reference.rs
Normal file
|
|
@ -0,0 +1,489 @@
|
|||
//! CPU reference rollout — the behavioral spec the WGSL shader mirrors.
|
||||
//!
|
||||
//! This module has zero wgpu dependencies and compiles on every build. It is
|
||||
//! the authority for what a 20-turn MCTS rollout produces given an
|
||||
//! `AbstractRolloutState` and a seed. The GPU path (Task C4 / #14) and the
|
||||
//! parity test (Task C5 / #15) both compare against this implementation.
|
||||
//!
|
||||
//! # Algorithm
|
||||
//!
|
||||
//! For each batch entry, the rollout runs for `ROLLOUT_TURNS` turns. Each turn,
|
||||
//! every player:
|
||||
//! 1. Enumerates the fixed 9-element `ActionKind::ALL` candidate set.
|
||||
//! 2. Computes `PersonalityPriors::action_prior` using the six axes packed
|
||||
//! into `AbstractPlayerState.axes[0..6]` (see axis-order note below).
|
||||
//! 3. Softmaxes the priors at temperature 1.0 and samples via Gumbel-max
|
||||
//! with the player's SplitMix64 RNG lane.
|
||||
//! 4. Applies integer-only effects to the player's `AbstractPlayerState`.
|
||||
//!
|
||||
//! After `ROLLOUT_TURNS`, each player's score is a weighted sum of their
|
||||
//! integer state fields (cities, pop, tech, force). Win probability for the
|
||||
//! "own" player (by convention index 0) is `sigmoid(own_score - best_enemy)`.
|
||||
//!
|
||||
//! # Determinism contract
|
||||
//!
|
||||
//! - All per-turn state mutations are integer arithmetic. Floats are only used
|
||||
//! at two points: (a) the softmax-over-priors action-sample, and (b) the
|
||||
//! terminal `sigmoid` that produces the win-prob output. Integer state is
|
||||
//! therefore byte-identical between CPU and GPU; only the final f32 is
|
||||
//! subject to tolerance.
|
||||
//! - RNG is per-player SplitMix64 using `gpu::splitmix`. Both CPU and WGSL
|
||||
//! step the state identically.
|
||||
//! - The priors formula is `PersonalityPriors::action_prior` — the canonical
|
||||
//! axis-to-action weighting frozen in Task B3 (#8). The WGSL mirror of this
|
||||
//! formula lives in `rollout.wgsl` (Task C3 / #13).
|
||||
//!
|
||||
//! # Axis ordering
|
||||
//!
|
||||
//! The POD comment (`abstract_state.rs:58`) describes `axes` using the
|
||||
//! evaluator's `AxisId` taxonomy (`expansion=0, production=1, wealth=2,
|
||||
//! culture=3`). For rollout-policy purposes we reinterpret slots 0..6 as the
|
||||
//! six `PersonalityPriors` axes in the order declared by that struct:
|
||||
//!
|
||||
//! | slot | axis |
|
||||
//! |------|---------------------|
|
||||
//! | 0 | aggression |
|
||||
//! | 1 | expansion |
|
||||
//! | 2 | production |
|
||||
//! | 3 | wealth |
|
||||
//! | 4 | trade_willingness |
|
||||
//! | 5 | grudge_persistence |
|
||||
//! | 6..7 | reserved (zero) |
|
||||
//!
|
||||
//! The upload path (Task #14) is responsible for writing the 6 axes into the
|
||||
//! POD in this order. Both the CPU reference and the WGSL kernel read them
|
||||
//! from this layout.
|
||||
|
||||
use crate::abstract_state::{AbstractPlayerState, AbstractRolloutState, MAX_PLAYERS};
|
||||
use crate::gpu::splitmix::{rand_f32, smix_step};
|
||||
use crate::policy::{ActionKind, PersonalityPriors};
|
||||
|
||||
/// Number of turns simulated per rollout. Matches Task C3 spec (20-turn stepper).
|
||||
pub const ROLLOUT_TURNS: u32 = 20;
|
||||
|
||||
/// Softmax temperature for action sampling. `1.0` matches the `policy.rs` test
|
||||
/// fixtures and keeps the distribution reasonably sharp without collapsing.
|
||||
pub const ACTION_TEMPERATURE: f32 = 1.0;
|
||||
|
||||
/// Score-to-win-prob sigmoid sharpness. Higher → more decisive cutoff around
|
||||
/// `own_score == best_enemy_score`. `0.10` gives a gentle S-curve over the
|
||||
/// typical integer score range (0..~100 after 20 turns).
|
||||
pub const WINPROB_SIGMOID_K: f32 = 0.10;
|
||||
|
||||
/// Which execution path produced a rollout result. Reported by
|
||||
/// `batch_simulate_gpu` / `batch_simulate_cpu` so callers (api-gdext,
|
||||
/// Phase-Gate proof scene) can surface it without re-inferring.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum RolloutPath {
|
||||
/// Computed via `batch_simulate_cpu`.
|
||||
Cpu,
|
||||
/// Computed via `batch_simulate_gpu` (wgpu dispatch succeeded).
|
||||
Gpu,
|
||||
}
|
||||
|
||||
impl RolloutPath {
|
||||
/// Stable string form for dictionaries / JSON. `"cpu"` or `"gpu"`.
|
||||
#[must_use]
|
||||
pub fn name(self) -> &'static str {
|
||||
match self {
|
||||
Self::Cpu => "cpu",
|
||||
Self::Gpu => "gpu",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl std::fmt::Display for RolloutPath {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
f.write_str(self.name())
|
||||
}
|
||||
}
|
||||
|
||||
/// Run the CPU reference rollout over a batch of starting states.
|
||||
///
|
||||
/// Each input state is simulated independently for `ROLLOUT_TURNS` turns using
|
||||
/// the RNG lanes already seeded in `state.players[*].rng_state`. The output
|
||||
/// has one f32 win-prob per input entry, tagged with `RolloutPath::Cpu`.
|
||||
///
|
||||
/// Input states are taken by value (not by ref) because the rollout mutates
|
||||
/// them — but only a local copy. The caller's state is unmodified.
|
||||
#[must_use]
|
||||
pub fn batch_simulate_cpu(batch: &[AbstractRolloutState]) -> Vec<(f32, RolloutPath)> {
|
||||
batch
|
||||
.iter()
|
||||
.map(|entry| (simulate_one(*entry), RolloutPath::Cpu))
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn simulate_one(mut state: AbstractRolloutState) -> f32 {
|
||||
for _ in 0..ROLLOUT_TURNS {
|
||||
step_turn(&mut state);
|
||||
}
|
||||
win_prob(&state, 0)
|
||||
}
|
||||
|
||||
fn step_turn(state: &mut AbstractRolloutState) {
|
||||
for pi in 0..MAX_PLAYERS {
|
||||
// Skip empty player slots — a player with zero cities and zero rng
|
||||
// is uninitialized; acting on them would leak action effects into
|
||||
// slots the caller meant to leave blank.
|
||||
if state.players[pi].city_count == 0 && state.players[pi].rng_state == 0 {
|
||||
continue;
|
||||
}
|
||||
let action = sample_action(&mut state.players[pi]);
|
||||
apply_action(state, pi, action);
|
||||
state.players[pi].turn = state.players[pi].turn.saturating_add(1);
|
||||
}
|
||||
}
|
||||
|
||||
/// Reconstruct `PersonalityPriors` from the first 6 bytes of `axes`.
|
||||
///
|
||||
/// A byte of `0` means "axis unset" — we map it to the neutral value `5.0`
|
||||
/// rather than treating it as an off-the-charts low axis. This matches
|
||||
/// `PersonalityPriors::from_axes` which defaults missing keys to 5.
|
||||
fn priors_from_axes(axes: [u8; 8]) -> PersonalityPriors {
|
||||
let axis = |b: u8| if b == 0 { 5.0 } else { b.clamp(1, 10) as f32 };
|
||||
PersonalityPriors {
|
||||
aggression: axis(axes[0]),
|
||||
expansion: axis(axes[1]),
|
||||
production: axis(axes[2]),
|
||||
wealth: axis(axes[3]),
|
||||
trade_willingness: axis(axes[4]),
|
||||
grudge_persistence: axis(axes[5]),
|
||||
}
|
||||
}
|
||||
|
||||
/// Sample one action via Gumbel-max: `argmax(log(p_i) + gumbel_i)`.
|
||||
/// Equivalent to softmax sampling but requires only one RNG draw per action
|
||||
/// candidate — exactly what the WGSL kernel can do without a loop over a
|
||||
/// random-index selection.
|
||||
fn sample_action(player: &mut AbstractPlayerState) -> ActionKind {
|
||||
let priors = priors_from_axes(player.axes);
|
||||
let t = ACTION_TEMPERATURE.max(0.05);
|
||||
|
||||
let mut best_kind = ActionKind::Idle;
|
||||
let mut best_score = f32::NEG_INFINITY;
|
||||
|
||||
for &kind in &ActionKind::ALL {
|
||||
let logit = priors.action_prior(kind) / t;
|
||||
player.rng_state = smix_step(player.rng_state);
|
||||
let u = rand_f32(player.rng_state).clamp(f32::EPSILON, 1.0 - f32::EPSILON);
|
||||
let gumbel = -(-u.ln()).ln();
|
||||
let score = logit + gumbel;
|
||||
if score > best_score {
|
||||
best_score = score;
|
||||
best_kind = kind;
|
||||
}
|
||||
}
|
||||
best_kind
|
||||
}
|
||||
|
||||
/// Apply integer-only effects. Opponent indexing uses a round-robin based on
|
||||
/// the acting player's `turn` field so the same action kind over successive
|
||||
/// turns spreads across opponents deterministically.
|
||||
fn apply_action(state: &mut AbstractRolloutState, pi: usize, action: ActionKind) {
|
||||
// Round-robin opponent: (pi+1) advances by turn%3 so we hit all three
|
||||
// non-self slots over time. `turn` has already been incremented for the
|
||||
// previous turn, so the first call sees `turn==0` and targets (pi+1)%4.
|
||||
let opp = {
|
||||
let turn = state.players[pi].turn as usize;
|
||||
let step = (turn % (MAX_PLAYERS - 1)) + 1;
|
||||
(pi + step) % MAX_PLAYERS
|
||||
};
|
||||
let p = &mut state.players[pi];
|
||||
|
||||
match action {
|
||||
ActionKind::Build => {
|
||||
p.gold = p.gold.saturating_add(1);
|
||||
// Tiny per-tier tick — rotates across tiers so production clans
|
||||
// don't pile everything into T1 over 20 turns.
|
||||
let tier = (p.turn as usize) % 4;
|
||||
p.unit_counts[tier] = p.unit_counts[tier].saturating_add(1);
|
||||
}
|
||||
ActionKind::Attack => {
|
||||
let other = &mut state.players[opp];
|
||||
other.force_rel[pi] = other.force_rel[pi].saturating_sub(1);
|
||||
state.players[pi].force_rel[opp] =
|
||||
state.players[pi].force_rel[opp].saturating_add(1);
|
||||
}
|
||||
ActionKind::Settle => {
|
||||
p.city_count = p.city_count.saturating_add(1);
|
||||
p.pop_total = p.pop_total.saturating_add(2);
|
||||
}
|
||||
ActionKind::Research => {
|
||||
p.science = p.science.saturating_add(1);
|
||||
if p.science >= 10 {
|
||||
p.tech_index = p.tech_index.saturating_add(1);
|
||||
p.science -= 10;
|
||||
}
|
||||
}
|
||||
ActionKind::Defend => {
|
||||
p.force_rel[pi] = p.force_rel[pi].saturating_add(1);
|
||||
p.happiness_pool = p.happiness_pool.saturating_add(1);
|
||||
}
|
||||
ActionKind::Trade => {
|
||||
p.gold = p.gold.saturating_add(2);
|
||||
}
|
||||
ActionKind::ContinueWar => {
|
||||
let other = &mut state.players[opp];
|
||||
other.relations[pi] = other.relations[pi].saturating_sub(1);
|
||||
state.players[pi].relations[opp] =
|
||||
state.players[pi].relations[opp].saturating_sub(1);
|
||||
state.players[pi].force_rel[opp] =
|
||||
state.players[pi].force_rel[opp].saturating_add(1);
|
||||
}
|
||||
ActionKind::MakePeace => {
|
||||
let other = &mut state.players[opp];
|
||||
other.relations[pi] = other.relations[pi].saturating_add(1);
|
||||
state.players[pi].relations[opp] =
|
||||
state.players[pi].relations[opp].saturating_add(1);
|
||||
}
|
||||
ActionKind::Idle => {
|
||||
p.happiness_pool = p.happiness_pool.saturating_add(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Terminal score for a player. Integer → f32 conversion deferred until the
|
||||
/// very last step so all state mutation stays bit-stable.
|
||||
fn score(p: &AbstractPlayerState) -> f32 {
|
||||
let cities = p.city_count as f32;
|
||||
let pop = p.pop_total as f32;
|
||||
let gold = p.gold as f32;
|
||||
let tech = p.tech_index as f32;
|
||||
let force: u32 = p.force_rel.iter().map(|&x| x as u32).sum();
|
||||
let units: u32 = p.unit_counts.iter().map(|&x| x as u32).sum();
|
||||
|
||||
10.0 * cities
|
||||
+ 0.5 * pop
|
||||
+ 0.1 * gold
|
||||
+ 5.0 * tech
|
||||
+ 0.2 * (force as f32)
|
||||
+ 0.3 * (units as f32)
|
||||
}
|
||||
|
||||
fn win_prob(state: &AbstractRolloutState, own_idx: usize) -> f32 {
|
||||
let own = score(&state.players[own_idx]);
|
||||
let best_enemy = (0..MAX_PLAYERS)
|
||||
.filter(|&i| i != own_idx)
|
||||
.map(|i| score(&state.players[i]))
|
||||
.fold(f32::NEG_INFINITY, f32::max);
|
||||
// If no enemies are present (all zero slots), win-prob defaults to 0.5.
|
||||
if !best_enemy.is_finite() {
|
||||
return 0.5;
|
||||
}
|
||||
let margin = (own - best_enemy) * WINPROB_SIGMOID_K;
|
||||
1.0 / (1.0 + (-margin).exp())
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use bytemuck::Zeroable;
|
||||
|
||||
fn make_player(pi: usize, axes: [u8; 8], seed: u64) -> AbstractPlayerState {
|
||||
let mut p = AbstractPlayerState::zeroed();
|
||||
p.axes = axes;
|
||||
p.rng_state = seed.wrapping_mul(pi as u64 + 1).wrapping_add(0xDEAD_BEEF);
|
||||
// Non-zero rng_state doubles as "slot is populated" so step_turn doesn't skip.
|
||||
p.city_count = 1;
|
||||
p
|
||||
}
|
||||
|
||||
/// Ironhold-like: high production, low aggression.
|
||||
fn ironhold_axes() -> [u8; 8] {
|
||||
[6, 4, 9, 3, 3, 7, 0, 0]
|
||||
}
|
||||
|
||||
/// Blackhammer-like: high aggression.
|
||||
fn blackhammer_axes() -> [u8; 8] {
|
||||
[9, 6, 7, 2, 2, 9, 0, 0]
|
||||
}
|
||||
|
||||
/// Goldvein-like: high trade and wealth.
|
||||
fn goldvein_axes() -> [u8; 8] {
|
||||
[3, 5, 5, 9, 9, 4, 0, 0]
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_batch_returns_empty() {
|
||||
let out = batch_simulate_cpu(&[]);
|
||||
assert!(out.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn all_results_are_cpu_path() {
|
||||
let mut state = AbstractRolloutState::zeroed();
|
||||
state.players[0] = make_player(0, ironhold_axes(), 42);
|
||||
state.players[1] = make_player(1, blackhammer_axes(), 42);
|
||||
let out = batch_simulate_cpu(&[state]);
|
||||
assert_eq!(out.len(), 1);
|
||||
assert_eq!(out[0].1, RolloutPath::Cpu);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn win_prob_is_in_unit_interval() {
|
||||
let mut state = AbstractRolloutState::zeroed();
|
||||
state.players[0] = make_player(0, ironhold_axes(), 1);
|
||||
state.players[1] = make_player(1, blackhammer_axes(), 1);
|
||||
state.players[2] = make_player(2, goldvein_axes(), 1);
|
||||
let out = batch_simulate_cpu(&[state]);
|
||||
let wp = out[0].0;
|
||||
assert!((0.0..=1.0).contains(&wp), "win prob {wp} out of [0,1]");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn determinism_same_input_same_output() {
|
||||
let mut state = AbstractRolloutState::zeroed();
|
||||
state.players[0] = make_player(0, ironhold_axes(), 123);
|
||||
state.players[1] = make_player(1, blackhammer_axes(), 123);
|
||||
let a = batch_simulate_cpu(&[state]);
|
||||
let b = batch_simulate_cpu(&[state]);
|
||||
assert_eq!(a[0].0.to_bits(), b[0].0.to_bits(),
|
||||
"same input must produce bit-identical win_prob");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn different_seeds_produce_different_outcomes() {
|
||||
// Two states identical except for per-player RNG seeds should produce
|
||||
// different win-probs across enough samples. We test with 16 distinct
|
||||
// seeds and require at least 3 unique outcomes (loose bar — the point
|
||||
// is "RNG actually threads through", not statistical power).
|
||||
let mut outcomes: std::collections::HashSet<u32> = Default::default();
|
||||
for seed in 0u64..16 {
|
||||
let mut state = AbstractRolloutState::zeroed();
|
||||
state.players[0] = make_player(0, ironhold_axes(), seed * 7 + 1);
|
||||
state.players[1] = make_player(1, blackhammer_axes(), seed * 7 + 1);
|
||||
state.players[2] = make_player(2, goldvein_axes(), seed * 7 + 1);
|
||||
let out = batch_simulate_cpu(&[state]);
|
||||
outcomes.insert(out[0].0.to_bits());
|
||||
}
|
||||
assert!(outcomes.len() >= 3,
|
||||
"expected RNG to produce >=3 distinct outcomes over 16 seeds, got {}",
|
||||
outcomes.len());
|
||||
}
|
||||
|
||||
/// Ironhold's high production axis biases Build → more units accumulated
|
||||
/// over 20 turns than a neutral personality with the same RNG seed.
|
||||
#[test]
|
||||
fn ironhold_accumulates_more_units_than_neutral() {
|
||||
let seed = 99u64;
|
||||
|
||||
// Neutral = all 5s
|
||||
let neutral_axes: [u8; 8] = [5, 5, 5, 5, 5, 5, 0, 0];
|
||||
|
||||
let mut neutral_state = AbstractRolloutState::zeroed();
|
||||
neutral_state.players[0] = make_player(0, neutral_axes, seed);
|
||||
neutral_state.players[1] = make_player(1, neutral_axes, seed);
|
||||
|
||||
let mut iron_state = AbstractRolloutState::zeroed();
|
||||
iron_state.players[0] = make_player(0, ironhold_axes(), seed);
|
||||
iron_state.players[1] = make_player(1, neutral_axes, seed);
|
||||
|
||||
// Direct inspection: run simulate_one visible mutation. We need to
|
||||
// expose intermediate state for the test; use a local simulator.
|
||||
fn count_units(mut s: AbstractRolloutState) -> u32 {
|
||||
for _ in 0..ROLLOUT_TURNS {
|
||||
step_turn(&mut s);
|
||||
}
|
||||
s.players[0].unit_counts.iter().map(|&x| x as u32).sum()
|
||||
}
|
||||
|
||||
// Average over 32 seeds so per-seed RNG noise doesn't flip the test.
|
||||
let mut neutral_total = 0u32;
|
||||
let mut iron_total = 0u32;
|
||||
for s in 0u64..32 {
|
||||
let mut n = AbstractRolloutState::zeroed();
|
||||
n.players[0] = make_player(0, neutral_axes, s * 13 + 7);
|
||||
n.players[1] = make_player(1, neutral_axes, s * 13 + 7);
|
||||
neutral_total += count_units(n);
|
||||
|
||||
let mut i = AbstractRolloutState::zeroed();
|
||||
i.players[0] = make_player(0, ironhold_axes(), s * 13 + 7);
|
||||
i.players[1] = make_player(1, neutral_axes, s * 13 + 7);
|
||||
iron_total += count_units(i);
|
||||
}
|
||||
assert!(
|
||||
iron_total > neutral_total,
|
||||
"Ironhold (prod=9) must build more units than neutral (prod=5) across 32 seeds: iron={iron_total} neutral={neutral_total}",
|
||||
);
|
||||
}
|
||||
|
||||
/// Blackhammer's high aggression axis biases Attack/ContinueWar → more
|
||||
/// force accumulation (force_rel entries) than a neutral personality.
|
||||
#[test]
|
||||
fn blackhammer_accumulates_more_force_than_neutral() {
|
||||
let neutral_axes: [u8; 8] = [5, 5, 5, 5, 5, 5, 0, 0];
|
||||
fn sum_force(mut s: AbstractRolloutState) -> u32 {
|
||||
for _ in 0..ROLLOUT_TURNS {
|
||||
step_turn(&mut s);
|
||||
}
|
||||
s.players[0].force_rel.iter().map(|&x| x as u32).sum()
|
||||
}
|
||||
|
||||
let mut neutral_total = 0u32;
|
||||
let mut bh_total = 0u32;
|
||||
for s in 0u64..32 {
|
||||
let mut n = AbstractRolloutState::zeroed();
|
||||
n.players[0] = make_player(0, neutral_axes, s * 17 + 3);
|
||||
n.players[1] = make_player(1, neutral_axes, s * 17 + 3);
|
||||
neutral_total += sum_force(n);
|
||||
|
||||
let mut b = AbstractRolloutState::zeroed();
|
||||
b.players[0] = make_player(0, blackhammer_axes(), s * 17 + 3);
|
||||
b.players[1] = make_player(1, neutral_axes, s * 17 + 3);
|
||||
bh_total += sum_force(b);
|
||||
}
|
||||
assert!(
|
||||
bh_total > neutral_total,
|
||||
"Blackhammer (agg=9) must accumulate more force than neutral across 32 seeds: bh={bh_total} neutral={neutral_total}",
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn priors_from_axes_neutral_on_zero() {
|
||||
let p = priors_from_axes([0, 0, 0, 0, 0, 0, 0, 0]);
|
||||
// All zero → all neutral (5.0) → every action_prior == 0.
|
||||
for k in ActionKind::ALL {
|
||||
let pr = p.action_prior(k);
|
||||
assert!(pr.abs() < 1e-5, "zero-axes prior for {k:?} was {pr}, expected 0");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn priors_from_axes_clamps_out_of_range() {
|
||||
// Byte values above 10 must clamp (defensive — upload path shouldn't
|
||||
// produce these, but we don't want a UB-free Rust prior and a
|
||||
// clamp-happy WGSL prior to drift).
|
||||
let p = priors_from_axes([200, 200, 200, 200, 200, 200, 0, 0]);
|
||||
assert!((p.aggression - 10.0).abs() < 1e-5);
|
||||
assert!((p.expansion - 10.0).abs() < 1e-5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_player_slots_are_skipped() {
|
||||
// A state with only player 0 populated (others zeroed) must still run
|
||||
// without crashing and produce a valid win-prob.
|
||||
let mut state = AbstractRolloutState::zeroed();
|
||||
state.players[0] = make_player(0, ironhold_axes(), 5);
|
||||
let out = batch_simulate_cpu(&[state]);
|
||||
let wp = out[0].0;
|
||||
assert!((0.0..=1.0).contains(&wp));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn batch_processing_preserves_entry_independence() {
|
||||
// Two identical states in one batch produce identical win-probs —
|
||||
// rollout doesn't leak state across batch entries.
|
||||
let mut state = AbstractRolloutState::zeroed();
|
||||
state.players[0] = make_player(0, ironhold_axes(), 88);
|
||||
state.players[1] = make_player(1, blackhammer_axes(), 88);
|
||||
let out = batch_simulate_cpu(&[state, state, state]);
|
||||
assert_eq!(out[0].0.to_bits(), out[1].0.to_bits());
|
||||
assert_eq!(out[1].0.to_bits(), out[2].0.to_bits());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn rollout_turns_constant_matches_spec() {
|
||||
assert_eq!(ROLLOUT_TURNS, 20, "Task C3 spec requires a 20-turn stepper");
|
||||
}
|
||||
}
|
||||
16
src/simulator/crates/mc-ai/src/gpu/mod.rs
Normal file
16
src/simulator/crates/mc-ai/src/gpu/mod.rs
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
//! GPU rollout module — home for `rollout.wgsl` + wgpu dispatch + CPU reference.
|
||||
//!
|
||||
//! The CPU reference (`cpu_reference`) is always available. It has zero wgpu
|
||||
//! dependencies and runs on every build. It is the behavioral spec the WGSL
|
||||
//! shader must match; the parity test (Task C5 / #15) compares `batch_simulate_gpu`
|
||||
//! against `batch_simulate_cpu` byte-for-byte on integer state and within a
|
||||
//! small tolerance on the final f32 win-probability.
|
||||
//!
|
||||
//! The `inner` module (feature-gated by `gpu`, landed in Task C2 / #12) holds
|
||||
//! the wgpu runtime — `GpuContext`, `dispatch_batch`, `rollout.wgsl`, buffer
|
||||
//! upload/readback. See `mc-turn/src/gpu/mod.rs` for the established pattern.
|
||||
|
||||
pub mod cpu_reference;
|
||||
pub mod splitmix;
|
||||
|
||||
pub use cpu_reference::{batch_simulate_cpu, RolloutPath};
|
||||
121
src/simulator/crates/mc-ai/src/gpu/splitmix.rs
Normal file
121
src/simulator/crates/mc-ai/src/gpu/splitmix.rs
Normal file
|
|
@ -0,0 +1,121 @@
|
|||
//! SplitMix64 RNG — the exact 64-bit stepper the WGSL rollout kernel uses.
|
||||
//!
|
||||
//! The WGSL mirror is `smix_step` in `mc-turn/src/gpu/fauna_encounter.wgsl`
|
||||
//! (reused verbatim when `rollout.wgsl` lands in Task C3). Both sides must
|
||||
//! advance per-step state identically so the Task C5 parity test can compare
|
||||
//! byte-for-byte.
|
||||
//!
|
||||
//! The Rust form uses `u64` wrapping arithmetic; WGSL lacks `u64` so it
|
||||
//! emulates the same operations via 32-bit schoolbook multiply. The invariants
|
||||
//! held in common:
|
||||
//!
|
||||
//! - Addend per step: `0xDEADBEEFCAFEBABE + 0x9E3779B97F4A7C15 = 0x7CE538A94A4936D3`
|
||||
//! (fauna salt + Weyl constant). For rollout we use a distinct salt so the
|
||||
//! two kernels never share an RNG lane; see `ROLLOUT_SALT` below.
|
||||
//! - Two xor-shift-mul mixing rounds with the Stafford-variant-13 constants.
|
||||
//! - Final xor-shift-right 31.
|
||||
//!
|
||||
//! `rand_f32` extracts the top 24 bits of the new state as a `[0, 1)` float.
|
||||
|
||||
/// The salt added per step for rollout RNG lanes. Distinct from the fauna salt
|
||||
/// (`0xDEADBEEFCAFEBABE`) so both kernels can run in the same frame without
|
||||
/// accidentally producing correlated streams.
|
||||
pub const ROLLOUT_SALT: u64 = 0xC0FFEE_F00D_BABE_D00D;
|
||||
|
||||
/// First Stafford-variant-13 mixing multiplier.
|
||||
const SMIX_MUL1: u64 = 0xBF58_476D_1CE4_E5B9;
|
||||
/// Second Stafford-variant-13 mixing multiplier.
|
||||
const SMIX_MUL2: u64 = 0x94D0_49BB_1331_11EB;
|
||||
|
||||
/// Advance the state by one SplitMix64 step using `ROLLOUT_SALT`. Returns the
|
||||
/// new state; the caller uses the high 32 bits via `rand_f32` to draw a float.
|
||||
#[must_use]
|
||||
pub fn smix_step(state: u64) -> u64 {
|
||||
let z = state.wrapping_add(ROLLOUT_SALT).wrapping_add(0x9E37_79B9_7F4A_7C15);
|
||||
let z = (z ^ (z >> 30)).wrapping_mul(SMIX_MUL1);
|
||||
let z = (z ^ (z >> 27)).wrapping_mul(SMIX_MUL2);
|
||||
z ^ (z >> 31)
|
||||
}
|
||||
|
||||
/// Extract a `[0.0, 1.0)` float from the high 24 bits of the post-step state.
|
||||
/// Matches WGSL `rand_f32` at `fauna_encounter.wgsl:135-137` bit-for-bit.
|
||||
#[must_use]
|
||||
pub fn rand_f32(state: u64) -> f32 {
|
||||
let bits = (state >> 40) as u32;
|
||||
bits as f32 / (1u32 << 24) as f32
|
||||
}
|
||||
|
||||
/// Step-and-draw convenience: advances state in place and returns the f32.
|
||||
#[must_use]
|
||||
pub fn step_and_draw(state: &mut u64) -> f32 {
|
||||
*state = smix_step(*state);
|
||||
rand_f32(*state)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn smix_step_is_deterministic() {
|
||||
let a = smix_step(0xDEAD_BEEF_CAFE_F00D);
|
||||
let b = smix_step(0xDEAD_BEEF_CAFE_F00D);
|
||||
assert_eq!(a, b, "smix_step must be pure");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn smix_step_advances_state() {
|
||||
let s = 0xDEAD_BEEF_CAFE_F00D;
|
||||
let s_next = smix_step(s);
|
||||
assert_ne!(s, s_next, "smix_step must change state");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn zero_state_is_degenerate_but_not_stuck() {
|
||||
// SplitMix64 tolerates zero state — returns a fixed non-zero value.
|
||||
// Callers must still seed properly; this test just documents the behavior.
|
||||
let s1 = smix_step(0);
|
||||
assert_ne!(s1, 0);
|
||||
let s2 = smix_step(s1);
|
||||
assert_ne!(s2, s1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn rand_f32_lies_in_unit_interval() {
|
||||
let mut s = 0x1234_5678_9ABC_DEF0;
|
||||
for _ in 0..4096 {
|
||||
let v = step_and_draw(&mut s);
|
||||
assert!((0.0..1.0).contains(&v), "draw {v} out of [0,1)");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn rand_f32_covers_full_range() {
|
||||
// Over many draws we should see values near 0 and near 1.
|
||||
let mut s = 0xAAAA_BBBB_CCCC_DDDD;
|
||||
let mut min = 1.0f32;
|
||||
let mut max = 0.0f32;
|
||||
for _ in 0..10_000 {
|
||||
let v = step_and_draw(&mut s);
|
||||
if v < min {
|
||||
min = v;
|
||||
}
|
||||
if v > max {
|
||||
max = v;
|
||||
}
|
||||
}
|
||||
assert!(min < 0.01, "expected near-0 draw in 10k samples, got min={min}");
|
||||
assert!(max > 0.99, "expected near-1 draw in 10k samples, got max={max}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn distinct_seeds_produce_distinct_sequences() {
|
||||
let mut a = 0x1111_1111_1111_1111;
|
||||
let mut b = 0x2222_2222_2222_2222;
|
||||
for _ in 0..8 {
|
||||
assert_ne!(smix_step(a), smix_step(b));
|
||||
a = smix_step(a);
|
||||
b = smix_step(b);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -11,10 +11,12 @@ pub mod game_state;
|
|||
pub mod mcts;
|
||||
pub mod mcts_tree;
|
||||
pub mod policy;
|
||||
pub mod rollout;
|
||||
|
||||
pub use abstract_state::{AbstractPlayerState, AbstractRolloutState, MAX_PLAYERS};
|
||||
pub use evaluator::{LoadError, PersonalityDef, ScoringWeights};
|
||||
pub use policy::{ActionKind, PersonalityPriors};
|
||||
pub use rollout::{GameRolloutState, DEFAULT_ROLLOUT_HORIZON, DEFAULT_ROLLOUT_TEMPERATURE};
|
||||
pub use game_state::{
|
||||
axes_to_flat, flat_to_axes, AiCityState, AiPlayerState, AiProductionCandidate,
|
||||
AiTechCandidate, AxisId, StrategicWeights,
|
||||
|
|
|
|||
|
|
@ -23,6 +23,25 @@ pub trait TreeState: Clone {
|
|||
fn is_terminal(&self) -> bool {
|
||||
self.legal_actions().is_empty()
|
||||
}
|
||||
|
||||
/// Walk a rollout from this state and return a reward in `[0, 1]`.
|
||||
///
|
||||
/// Default returns `0.5` — the historical stub. States that implement a
|
||||
/// real rollout (e.g. `rollout::GameRolloutState`) override this with a
|
||||
/// softmax-sampled trajectory that honors personality priors.
|
||||
///
|
||||
/// Parameters are threaded from `Tree::rollout_*` config knobs so the
|
||||
/// tree engine can tune softmax temperature and horizon per run without
|
||||
/// requiring every `TreeState` impl to accept them.
|
||||
fn rollout(
|
||||
&self,
|
||||
_rng: &mut XorShift64,
|
||||
_horizon: u32,
|
||||
_temperature: f32,
|
||||
_root_player: u8,
|
||||
) -> f32 {
|
||||
0.5
|
||||
}
|
||||
}
|
||||
|
||||
/// Tree node. `children` holds indices into the owning arena (`Tree::nodes`).
|
||||
|
|
@ -52,6 +71,16 @@ impl<S: TreeState> Node<S> {
|
|||
pub struct Tree<S: TreeState> {
|
||||
pub nodes: Vec<Node<S>>,
|
||||
pub exploration_constant: f32,
|
||||
/// Maximum simulated turns walked per rollout. Passed into
|
||||
/// `TreeState::rollout`. Defaults to `rollout::DEFAULT_ROLLOUT_HORIZON`.
|
||||
pub rollout_horizon: u32,
|
||||
/// Softmax temperature for action sampling inside rollouts. Lower =
|
||||
/// sharper peak on highest-prior action. Defaults to
|
||||
/// `rollout::DEFAULT_ROLLOUT_TEMPERATURE`.
|
||||
pub rollout_temperature: f32,
|
||||
/// Index of the player MCTS is deciding for. Rewards in `simulate()`
|
||||
/// are evaluated from this player's perspective.
|
||||
pub root_player: u8,
|
||||
}
|
||||
|
||||
impl<S: TreeState> Tree<S> {
|
||||
|
|
@ -59,6 +88,9 @@ impl<S: TreeState> Tree<S> {
|
|||
Self {
|
||||
nodes: vec![Node::new(root_state, None, None)],
|
||||
exploration_constant: std::f32::consts::SQRT_2,
|
||||
rollout_horizon: crate::rollout::DEFAULT_ROLLOUT_HORIZON,
|
||||
rollout_temperature: crate::rollout::DEFAULT_ROLLOUT_TEMPERATURE,
|
||||
root_player: 0,
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -110,10 +142,20 @@ impl<S: TreeState> Tree<S> {
|
|||
Some(child_idx)
|
||||
}
|
||||
|
||||
/// Stubbed rollout. Returns 0.5 regardless of state until game simulation lands.
|
||||
/// `_rng` is threaded so future random rollouts slot in without API changes.
|
||||
pub fn simulate(&self, _idx: usize, _rng: &mut XorShift64) -> f32 {
|
||||
0.5
|
||||
/// Run a rollout from the target node and return its reward in `[0, 1]`.
|
||||
///
|
||||
/// Delegates to `TreeState::rollout` with the tree's configured horizon,
|
||||
/// temperature, and root player. States that leave the default impl in
|
||||
/// place still get the historical `0.5` stub; states overriding `rollout`
|
||||
/// (see `rollout::GameRolloutState`) walk a real softmax-sampled
|
||||
/// trajectory.
|
||||
pub fn simulate(&self, idx: usize, rng: &mut XorShift64) -> f32 {
|
||||
self.nodes[idx].state.rollout(
|
||||
rng,
|
||||
self.rollout_horizon,
|
||||
self.rollout_temperature,
|
||||
self.root_player,
|
||||
)
|
||||
}
|
||||
|
||||
/// Propagate `reward` from `idx` up to the root, incrementing visits and wins.
|
||||
|
|
|
|||
466
src/simulator/crates/mc-ai/src/rollout.rs
Normal file
466
src/simulator/crates/mc-ai/src/rollout.rs
Normal file
|
|
@ -0,0 +1,466 @@
|
|||
//! Task A2 — CPU shallow rollout using `AbstractRolloutState`.
|
||||
//!
|
||||
//! The MCTS tree now walks `RolloutHorizon` simulated turns per leaf instead
|
||||
//! of returning the 0.5 stub. Each rollout step samples an action kind from
|
||||
//! a clan-biased softmax (driven by `policy::PersonalityPriors::action_prior`)
|
||||
//! and mutates the compact state via coarse per-action rules. Terminal reward
|
||||
//! is a clan-blind score over gold / science / pop / cities / force dominance,
|
||||
//! normalized to `[0, 1]` so UCB1 math stays well-conditioned.
|
||||
//!
|
||||
//! The rollout state is **not** `AbstractRolloutState` directly — it's a
|
||||
//! sidecar wrapper `GameRolloutState` that carries the compact POD plus a
|
||||
//! `[PersonalityPriors; MAX_PLAYERS]` side-table. This keeps the POD's
|
||||
//! `bytemuck::Pod` / `#[repr(C)]` contract pristine for gpu-dev's WGSL
|
||||
//! mirror (`rollout.wgsl`, Task C3) while still letting the CPU rollout
|
||||
//! read per-player priors without heap allocation.
|
||||
//!
|
||||
//! Determinism: each rollout is seeded via XorShift64. Same seed → same
|
||||
//! action distribution → same reward, across repeated invocations and
|
||||
//! across threads (see `mcts_tree::Tree::simulate_parallel`).
|
||||
|
||||
use crate::abstract_state::{AbstractPlayerState, AbstractRolloutState, MAX_PLAYERS};
|
||||
use crate::mcts::XorShift64;
|
||||
use crate::mcts_tree::TreeState;
|
||||
use crate::policy::{ActionKind, PersonalityPriors};
|
||||
|
||||
/// Default number of simulated turns walked per rollout. Tuned in concert
|
||||
/// with `Tree::rollout_temperature`; raising this exposes deeper strategic
|
||||
/// consequences at linear cost.
|
||||
pub const DEFAULT_ROLLOUT_HORIZON: u32 = 20;
|
||||
|
||||
/// Default softmax temperature for rollout action sampling. `1.0` matches
|
||||
/// the fixture tests in `tests/clan_policy_priors.rs`. Lower = sharper peak
|
||||
/// on the preferred action; higher = more exploration.
|
||||
pub const DEFAULT_ROLLOUT_TEMPERATURE: f32 = 1.0;
|
||||
|
||||
/// Bundled rollout state: compact POD payload + per-player personality priors.
|
||||
/// `GameRolloutState` owns `PersonalityPriors` per player so rollouts can bias
|
||||
/// action selection without touching the `AbstractRolloutState` POD.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct GameRolloutState {
|
||||
/// GPU-uploadable compact player state. Unchanged across the rollout
|
||||
/// when the tree clones this struct at each expansion.
|
||||
pub abstract_state: AbstractRolloutState,
|
||||
/// Per-player personality axes. Indexed by player slot; slot 0 is the
|
||||
/// MCTS root player by convention, but the rollout honors all players'
|
||||
/// priors so opponents behave in-character too.
|
||||
pub priors: [PersonalityPriors; MAX_PLAYERS],
|
||||
/// Active player index — whose turn it is for the *next* `apply`.
|
||||
/// Rotates modulo `MAX_PLAYERS` each step.
|
||||
pub active_player: u8,
|
||||
/// Number of simulated turns walked so far. Compared against
|
||||
/// `Tree::rollout_horizon` to detect termination without needing
|
||||
/// `horizon` to live inside the POD.
|
||||
pub depth: u32,
|
||||
}
|
||||
|
||||
impl GameRolloutState {
|
||||
/// Construct a rollout state from a seeded POD and per-player priors.
|
||||
/// `active_player` starts at 0; `depth` starts at 0.
|
||||
#[must_use]
|
||||
pub fn new(
|
||||
abstract_state: AbstractRolloutState,
|
||||
priors: [PersonalityPriors; MAX_PLAYERS],
|
||||
) -> Self {
|
||||
Self { abstract_state, priors, active_player: 0, depth: 0 }
|
||||
}
|
||||
|
||||
/// Read the active player's compact state.
|
||||
fn active(&self) -> &AbstractPlayerState {
|
||||
&self.abstract_state.players[self.active_player as usize]
|
||||
}
|
||||
|
||||
/// Mutable borrow of the active player's compact state.
|
||||
fn active_mut(&mut self) -> &mut AbstractPlayerState {
|
||||
&mut self.abstract_state.players[self.active_player as usize]
|
||||
}
|
||||
|
||||
/// Return the list of kinds legal for the active player given current
|
||||
/// resources. Kept deliberately coarse — the MCTS rollout only needs
|
||||
/// action *categories*, not per-id candidate filtering.
|
||||
///
|
||||
/// Legality rules:
|
||||
/// - `Build` and `Research` are always available (zero-cost fallbacks).
|
||||
/// - `Attack` / `ContinueWar` require some force_rel > 0 against an opponent.
|
||||
/// - `Settle` requires gold ≥ 40 (founding cost).
|
||||
/// - `Trade` requires gold ≥ 0 (no deficit-trading).
|
||||
/// - `Defend` and `Idle` are always available.
|
||||
/// - `MakePeace` only appears when any relation is < 0 (at war).
|
||||
pub fn active_actions(&self) -> Vec<ActionKind> {
|
||||
let p = self.active();
|
||||
let mut out: Vec<ActionKind> = Vec::with_capacity(9);
|
||||
out.push(ActionKind::Build);
|
||||
out.push(ActionKind::Research);
|
||||
out.push(ActionKind::Defend);
|
||||
out.push(ActionKind::Idle);
|
||||
let has_enemy_force = p.force_rel.iter().any(|&f| f > 0);
|
||||
if has_enemy_force {
|
||||
out.push(ActionKind::Attack);
|
||||
out.push(ActionKind::ContinueWar);
|
||||
}
|
||||
if p.gold >= 40 {
|
||||
out.push(ActionKind::Settle);
|
||||
}
|
||||
if p.gold >= 0 {
|
||||
out.push(ActionKind::Trade);
|
||||
}
|
||||
if p.relations.iter().any(|&r| r < 0) {
|
||||
out.push(ActionKind::MakePeace);
|
||||
}
|
||||
out
|
||||
}
|
||||
|
||||
/// Apply an action for the active player, mutating the compact state in
|
||||
/// place. Rules are intentionally linear so the CPU path and WGSL
|
||||
/// kernel can match numerics bit-for-bit (Task C5 parity).
|
||||
pub fn apply_active(&mut self, action: ActionKind) {
|
||||
{
|
||||
let p = self.active_mut();
|
||||
match action {
|
||||
ActionKind::Build => {
|
||||
p.pop_total = p.pop_total.saturating_add(1);
|
||||
p.science = p.science.saturating_add(5);
|
||||
p.gold = p.gold.saturating_sub(10);
|
||||
}
|
||||
ActionKind::Attack => {
|
||||
// Subtract 10 relative force from whichever opponent has
|
||||
// the highest current force_rel vs us.
|
||||
let idx = highest_force_index(&p.force_rel);
|
||||
p.force_rel[idx] = p.force_rel[idx].saturating_sub(10);
|
||||
p.happiness_pool = p.happiness_pool.saturating_sub(2);
|
||||
p.gold = p.gold.saturating_sub(15);
|
||||
// Flip relations to war with the attacked slot.
|
||||
p.relations[idx] = -1_i8.max(p.relations[idx].saturating_sub(1));
|
||||
}
|
||||
ActionKind::Settle => {
|
||||
p.city_count = p.city_count.saturating_add(1);
|
||||
p.pop_total = p.pop_total.saturating_add(2);
|
||||
p.gold = p.gold.saturating_sub(40);
|
||||
p.science = p.science.saturating_add(3);
|
||||
}
|
||||
ActionKind::Research => {
|
||||
p.science = p.science.saturating_add(15);
|
||||
// Science overflow advances the tech index up to 100%.
|
||||
if p.science >= 100 {
|
||||
let overflow = p.science / 100;
|
||||
p.science -= overflow * 100;
|
||||
p.tech_index = (p.tech_index.saturating_add(overflow as u16)).min(100);
|
||||
}
|
||||
}
|
||||
ActionKind::Defend => {
|
||||
p.happiness_pool = p.happiness_pool.saturating_add(1);
|
||||
// Shore up force_rel against the highest enemy by 2.
|
||||
let idx = highest_force_index(&p.force_rel);
|
||||
p.force_rel[idx] = p.force_rel[idx].saturating_add(2);
|
||||
p.gold = p.gold.saturating_sub(5);
|
||||
}
|
||||
ActionKind::Trade => {
|
||||
p.gold = p.gold.saturating_add(20);
|
||||
p.science = p.science.saturating_add(2);
|
||||
}
|
||||
ActionKind::ContinueWar => {
|
||||
let idx = highest_force_index(&p.force_rel);
|
||||
p.force_rel[idx] = p.force_rel[idx].saturating_sub(5);
|
||||
p.happiness_pool = p.happiness_pool.saturating_sub(1);
|
||||
}
|
||||
ActionKind::MakePeace => {
|
||||
// Flip the first negative relation back to 0 (peace).
|
||||
for r in p.relations.iter_mut() {
|
||||
if *r < 0 {
|
||||
*r = 0;
|
||||
break;
|
||||
}
|
||||
}
|
||||
p.happiness_pool = p.happiness_pool.saturating_add(3);
|
||||
p.gold = p.gold.saturating_add(5);
|
||||
}
|
||||
ActionKind::Idle => {
|
||||
p.gold = p.gold.saturating_add(2);
|
||||
}
|
||||
}
|
||||
p.turn = p.turn.saturating_add(1);
|
||||
}
|
||||
self.active_player = (self.active_player + 1) % (MAX_PLAYERS as u8);
|
||||
self.depth = self.depth.saturating_add(1);
|
||||
}
|
||||
|
||||
/// Clan-blind score of `for_player`'s compact state, in `[0, 1]`.
|
||||
/// The function is monotonic in gold / science / pop / cities / force
|
||||
/// dominance; clan differences come from the rollout's action trajectory,
|
||||
/// not the terminal evaluator.
|
||||
pub fn score_player(&self, for_player: u8) -> f32 {
|
||||
let p = &self.abstract_state.players[for_player as usize];
|
||||
let gold = (p.gold.max(0) as f32) * 0.02;
|
||||
let science = (p.science.max(0) as f32) * 0.03;
|
||||
let pop = (p.pop_total as f32) * 0.50;
|
||||
let cities = (p.city_count as f32) * 3.00;
|
||||
let tech = (p.tech_index as f32) * 0.10;
|
||||
let happy = (p.happiness_pool as f32) * 0.20;
|
||||
// Relative force dominance: max of our attacks minus mean of opponents'.
|
||||
let force_sum: f32 = p.force_rel.iter().map(|&f| f as f32).sum();
|
||||
let force = force_sum * 0.05;
|
||||
let raw = gold + science + pop + cities + tech + happy + force;
|
||||
// Squash to [0, 1] via soft saturation.
|
||||
raw / (1.0 + raw.abs())
|
||||
}
|
||||
|
||||
/// Prior score for (action, for_player) — thin wrapper around
|
||||
/// `PersonalityPriors::action_prior`. Exposed as an inherent method so
|
||||
/// the MCTS tree's `rollout_step` can ask for priors without needing a
|
||||
/// trait-level method on `TreeState` (which would break the toy tests).
|
||||
pub fn action_prior(&self, action: ActionKind, for_player: u8) -> f32 {
|
||||
self.priors[for_player as usize].action_prior(action)
|
||||
}
|
||||
}
|
||||
|
||||
/// Index into `force_rel` with the largest value. Ties broken by lowest index.
|
||||
fn highest_force_index(force_rel: &[u16; 4]) -> usize {
|
||||
let mut best = 0_usize;
|
||||
let mut best_v = force_rel[0];
|
||||
for (i, &v) in force_rel.iter().enumerate().skip(1) {
|
||||
if v > best_v {
|
||||
best = i;
|
||||
best_v = v;
|
||||
}
|
||||
}
|
||||
best
|
||||
}
|
||||
|
||||
impl TreeState for GameRolloutState {
|
||||
type Action = ActionKind;
|
||||
|
||||
fn legal_actions(&self) -> Vec<Self::Action> {
|
||||
self.active_actions()
|
||||
}
|
||||
|
||||
fn apply(&self, action: &Self::Action) -> Self {
|
||||
let mut next = *self;
|
||||
next.apply_active(*action);
|
||||
next
|
||||
}
|
||||
|
||||
fn is_terminal(&self) -> bool {
|
||||
// Rollout horizon is owned by the tree engine, not the state. Tree
|
||||
// drives termination via `Tree::rollout_horizon` — this fallback
|
||||
// only triggers on pathological states (all force_rel zero AND
|
||||
// gold starved across all players) to keep the toy/default path
|
||||
// well-behaved if someone calls `is_terminal` directly.
|
||||
let all_dead = self
|
||||
.abstract_state
|
||||
.players
|
||||
.iter()
|
||||
.all(|p| p.gold < -200 && p.pop_total == 0);
|
||||
all_dead
|
||||
}
|
||||
|
||||
fn rollout(
|
||||
&self,
|
||||
rng: &mut XorShift64,
|
||||
horizon: u32,
|
||||
temperature: f32,
|
||||
root_player: u8,
|
||||
) -> f32 {
|
||||
walk(self, rng, horizon, temperature, root_player)
|
||||
}
|
||||
}
|
||||
|
||||
/// Stand-alone rollout walker usable from `mcts_tree::Tree::simulate` OR
|
||||
/// from a plain test that wants to measure action-distribution divergence.
|
||||
/// Returns the terminal reward for `root_player` after walking `horizon`
|
||||
/// steps (or until `is_terminal` trips) with softmax-sampled actions.
|
||||
///
|
||||
/// Determinism: `rng` is threaded throughout; same seed + same starting
|
||||
/// state + same priors → same reward.
|
||||
pub fn walk(
|
||||
start: &GameRolloutState,
|
||||
rng: &mut XorShift64,
|
||||
horizon: u32,
|
||||
temperature: f32,
|
||||
root_player: u8,
|
||||
) -> f32 {
|
||||
let mut state = *start;
|
||||
for _ in 0..horizon {
|
||||
if TreeState::is_terminal(&state) {
|
||||
break;
|
||||
}
|
||||
let actions = state.active_actions();
|
||||
if actions.is_empty() {
|
||||
break;
|
||||
}
|
||||
let active = state.active_player;
|
||||
let kinds: Vec<ActionKind> = actions.clone();
|
||||
let dist = state
|
||||
.priors[active as usize]
|
||||
.action_distribution(&kinds, temperature);
|
||||
let pick = sample_categorical(&dist, rng);
|
||||
state.apply_active(actions[pick]);
|
||||
}
|
||||
state.score_player(root_player)
|
||||
}
|
||||
|
||||
/// Categorical sampler: walk the cumulative distribution until a random draw
|
||||
/// in `[0, 1)` is exceeded. `dist` is expected to sum to 1.0 (±fp noise);
|
||||
/// on degenerate input the last index is returned.
|
||||
fn sample_categorical(dist: &[f32], rng: &mut XorShift64) -> usize {
|
||||
if dist.is_empty() {
|
||||
return 0;
|
||||
}
|
||||
let draw = rng.next_f32();
|
||||
let mut cum = 0.0_f32;
|
||||
for (i, &p) in dist.iter().enumerate() {
|
||||
cum += p;
|
||||
if draw < cum {
|
||||
return i;
|
||||
}
|
||||
}
|
||||
dist.len() - 1
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_state(priors_ironhold: bool) -> GameRolloutState {
|
||||
let mut pod = AbstractRolloutState::zeroed();
|
||||
// Player 0 starts with enough resources to exercise every action.
|
||||
pod.players[0].gold = 100;
|
||||
pod.players[0].pop_total = 5;
|
||||
pod.players[0].city_count = 1;
|
||||
pod.players[0].force_rel = [0, 20, 0, 0]; // at odds with slot 1
|
||||
pod.players[0].relations = [0, -1, 0, 0];
|
||||
pod.players[0].rng_state = 0xAAAA_BBBB_CCCC_DDDD;
|
||||
pod.players[0].turn = 1;
|
||||
// Opponent slot 1 also seeded lightly.
|
||||
pod.players[1].gold = 50;
|
||||
pod.players[1].pop_total = 3;
|
||||
pod.players[1].rng_state = 0x1111_2222_3333_4444;
|
||||
pod.players[1].turn = 1;
|
||||
|
||||
let ironhold = PersonalityPriors {
|
||||
aggression: 6.0,
|
||||
expansion: 4.0,
|
||||
production: 9.0,
|
||||
wealth: 3.0,
|
||||
trade_willingness: 3.0,
|
||||
grudge_persistence: 7.0,
|
||||
};
|
||||
let blackhammer = PersonalityPriors {
|
||||
aggression: 9.0,
|
||||
expansion: 6.0,
|
||||
production: 7.0,
|
||||
wealth: 2.0,
|
||||
trade_willingness: 2.0,
|
||||
grudge_persistence: 9.0,
|
||||
};
|
||||
let priors = if priors_ironhold {
|
||||
[ironhold; MAX_PLAYERS]
|
||||
} else {
|
||||
[blackhammer; MAX_PLAYERS]
|
||||
};
|
||||
GameRolloutState::new(pod, priors)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn active_actions_respects_gold_gate() {
|
||||
let mut s = make_state(true);
|
||||
s.abstract_state.players[0].gold = 10; // below Settle threshold (40)
|
||||
let acts = s.active_actions();
|
||||
assert!(!acts.contains(&ActionKind::Settle));
|
||||
assert!(acts.contains(&ActionKind::Build));
|
||||
assert!(acts.contains(&ActionKind::Trade)); // gold >= 0
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn active_actions_gates_make_peace_on_war() {
|
||||
let mut s = make_state(true);
|
||||
s.abstract_state.players[0].relations = [0, 0, 0, 0]; // all at peace
|
||||
assert!(!s.active_actions().contains(&ActionKind::MakePeace));
|
||||
s.abstract_state.players[0].relations = [0, -1, 0, 0]; // at war with slot 1
|
||||
assert!(s.active_actions().contains(&ActionKind::MakePeace));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn apply_build_advances_pop_and_science() {
|
||||
let mut s = make_state(true);
|
||||
let before_pop = s.abstract_state.players[0].pop_total;
|
||||
let before_science = s.abstract_state.players[0].science;
|
||||
s.apply_active(ActionKind::Build);
|
||||
assert_eq!(s.abstract_state.players[0].pop_total, before_pop + 1);
|
||||
assert_eq!(s.abstract_state.players[0].science, before_science + 5);
|
||||
assert_eq!(s.active_player, 1);
|
||||
assert_eq!(s.depth, 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn apply_settle_deducts_gold_and_adds_city() {
|
||||
let mut s = make_state(true);
|
||||
s.apply_active(ActionKind::Settle);
|
||||
assert_eq!(s.abstract_state.players[0].city_count, 2);
|
||||
assert_eq!(s.abstract_state.players[0].gold, 60);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn research_overflow_advances_tech_index() {
|
||||
let mut s = make_state(true);
|
||||
s.abstract_state.players[0].science = 95;
|
||||
s.apply_active(ActionKind::Research);
|
||||
// 95 + 15 = 110 → tech advances by 1, science carries 10
|
||||
assert_eq!(s.abstract_state.players[0].tech_index, 1);
|
||||
assert_eq!(s.abstract_state.players[0].science, 10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn walk_deterministic_for_same_seed() {
|
||||
let start = make_state(true);
|
||||
let mut rng_a = XorShift64::new(42);
|
||||
let reward_a = walk(&start, &mut rng_a, 20, 1.0, 0);
|
||||
let mut rng_b = XorShift64::new(42);
|
||||
let reward_b = walk(&start, &mut rng_b, 20, 1.0, 0);
|
||||
assert!((reward_a - reward_b).abs() < 1e-6, "walk must be seed-deterministic");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn walk_returns_finite_nonzero_reward() {
|
||||
let start = make_state(true);
|
||||
let mut rng = XorShift64::new(7);
|
||||
let reward = walk(&start, &mut rng, 20, 1.0, 0);
|
||||
assert!(reward.is_finite(), "reward must be finite, got {reward}");
|
||||
assert!(
|
||||
(0.0..=1.0).contains(&reward),
|
||||
"reward must be in [0, 1], got {reward}"
|
||||
);
|
||||
assert!(
|
||||
reward > 0.0,
|
||||
"a starting state with positive resources must produce reward > 0"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn score_player_rewards_gold_and_pop() {
|
||||
let mut s = make_state(true);
|
||||
let low = s.score_player(0);
|
||||
s.abstract_state.players[0].gold = 500;
|
||||
s.abstract_state.players[0].pop_total = 30;
|
||||
let high = s.score_player(0);
|
||||
assert!(high > low, "higher gold/pop must score higher: low={low} high={high}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn score_player_stays_in_unit_interval() {
|
||||
let mut s = make_state(true);
|
||||
s.abstract_state.players[0].gold = i32::MAX / 2;
|
||||
s.abstract_state.players[0].pop_total = u32::MAX / 2;
|
||||
let r = s.score_player(0);
|
||||
assert!((0.0..=1.0).contains(&r), "score must saturate to [0, 1], got {r}");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sample_categorical_is_deterministic() {
|
||||
let dist = [0.25_f32, 0.50, 0.25];
|
||||
let mut rng_a = XorShift64::new(100);
|
||||
let mut rng_b = XorShift64::new(100);
|
||||
for _ in 0..20 {
|
||||
assert_eq!(sample_categorical(&dist, &mut rng_a), sample_categorical(&dist, &mut rng_b));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -154,8 +154,24 @@ def render(objectives: list[Objective]) -> str:
|
|||
"p1": "P1 — Ship-readiness",
|
||||
"p2": "P2 — Polish",
|
||||
}
|
||||
|
||||
def render_row(o: Objective) -> str:
|
||||
link = f"[{o.id}]({o.path.name})"
|
||||
icon = STATUS_ICON[o.status]
|
||||
owner_cell = (
|
||||
f"[{o.owner}](../team-leads/{o.owner}.md)"
|
||||
if o.owner else "—"
|
||||
)
|
||||
return (
|
||||
f"| {link} | {icon} {o.status} | {o.title} "
|
||||
f"| {owner_cell} | {o.updated_at} |"
|
||||
)
|
||||
|
||||
# Priority sections render only in-scope (non-oos) objectives. OOS items
|
||||
# are collected and rendered in a separate trailing section so they don't
|
||||
# compete with active work for attention.
|
||||
for prio in ("p0", "p1", "p2"):
|
||||
group = by_priority[prio]
|
||||
group = [o for o in by_priority[prio] if o.status != "oos"]
|
||||
if not group:
|
||||
continue
|
||||
lines.append(f"## {priority_heading[prio]}")
|
||||
|
|
@ -163,16 +179,24 @@ def render(objectives: list[Objective]) -> str:
|
|||
lines.append("| ID | Status | Title | Owner | Updated |")
|
||||
lines.append("|---|---|---|---|---|")
|
||||
for o in group:
|
||||
link = f"[{o.id}]({o.path.name})"
|
||||
icon = STATUS_ICON[o.status]
|
||||
owner_cell = (
|
||||
f"[{o.owner}](../team-leads/{o.owner}.md)"
|
||||
if o.owner else "—"
|
||||
)
|
||||
lines.append(
|
||||
f"| {link} | {icon} {o.status} | {o.title} "
|
||||
f"| {owner_cell} | {o.updated_at} |"
|
||||
)
|
||||
lines.append(render_row(o))
|
||||
lines.append("")
|
||||
|
||||
oos_items = [o for o in objectives if o.status == "oos"]
|
||||
if oos_items:
|
||||
lines.append("## Out of Scope (Game 2)")
|
||||
lines.append("")
|
||||
lines.append(
|
||||
"> These objectives are explicitly future-scope for **Game 2 "
|
||||
"(Age of Kzzykt)**. They are **not** part of the Game 1 Early "
|
||||
"Access release and are listed only for reference. Do not treat "
|
||||
"them as priorities."
|
||||
)
|
||||
lines.append("")
|
||||
lines.append("| ID | Status | Title | Owner | Updated |")
|
||||
lines.append("|---|---|---|---|---|")
|
||||
for o in oos_items:
|
||||
lines.append(render_row(o))
|
||||
lines.append("")
|
||||
|
||||
return "\n".join(lines) + "\n"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue