katgpt-rs: Monopoly FSM Arena — 4-Player Heuristic Learning Board Game Engine

Overview

A complete Monopoly board game engine using bevy_ecs standalone (not the full Bevy engine) for deterministic, turn-based simulation. Four AI players compete at progressively higher HL technology levels across a fully implemented 40-square board with cards, auctions, trades, mortgages, and house building.

The engine serves as the second integration test bed for the HL thesis: bandit-driven strategy selection + game phase adaptation + opponent modeling > static heuristics or random baselines in a turn-based strategy domain.

Feature flag: monopoly = ["bevy_ecs", "bandit"].

Architecture

Game Loop

All systems operate on &mut World directly — no ECS schedule, no real-time delta, no plugins.

init_world(seed)
  ├─ GameConfig, TurnState, Statistics  → resources
  ├─ Events<GameEvent>                  → event bus
  ├─ build_board()                      → 40 BoardSquare entities
  ├─ shuffle_decks(seed)                → Chance + CommunityChest CardDeck entities
  └─ spawn_players()                    → 4 Player entities at GO with $1500

run_game(seed, players, rng, max_turns) → GameResult
  ├─ reset() all players
  └─ loop: execute_turn() for each active player
       ├─ count_active_players() → 1? → find_winner()
       └─ turn >= max_turns → find_richest()

execute_turn(world, player_id, ai, rng) → TurnResult
  ├─ Phase 1: PreTurn (jail decision)
  ├─ Phase 2–4: Rolling / Resolving / Doubles loop
  ├─ Phase 5: Strategic (build houses)
  └─ Phase 6: EndTurn

FSM Phases (Sequential, not Priority-Based)

Unlike Bomberman's priority-based tick FSM, Monopoly uses a sequential phase pipeline — each phase runs once per turn in order:

PreTurn ──→ Rolling ──→ Resolving ──→ Strategic ──→ EndTurn
    │                        │
    │  jail_decision()       │  resolve_landing()
    │  pay/roll/card         │  property/auction/card/tax
    │                        │
    └── doubles? ────────────┘  (re-roll loop)

Phase	Purpose	AI Hook
PreTurn	Jail decision: pay fine, use card, or roll for doubles	`jail_decision()`
Rolling	Roll dice, track doubles count, send to jail on 3rd double	—
Resolving	Move token, collect salary, resolve square landing	`should_buy_property()`, `auction_bid()`, `trade_response()`
Strategic	Build houses on owned monopolies	`build_houses()`, `propose_trade()`
EndTurn	Advance to next active player	—

The doubles loop re-enters Rolling→Resolving when doubles are rolled (max 3 before Speeding jail).

Board Layout

40 Squares

 0  GO                10 Jail              20 Free Parking      30 Go To Jail
 1  Mediterranean Ave  11 St. Charles Pl    21 Kentucky Ave       31 Pacific Ave
 2  Community Chest    12 Electric Co       22 Chance             32 North Carolina Ave
 3  Baltic Ave         13 States Ave        23 Indiana Ave        33 Community Chest
 4  Income Tax         14 Virginia Ave      24 Illinois Ave       34 Pennsylvania Ave
 5  Reading Railroad   15 Pennsylvania RR   25 B&O Railroad       35 Short Line RR
 6  Oriental Ave       16 St. James Pl      26 Atlantic Ave       36 Chance
 7  Chance             17 Community Chest   27 Ventnor Ave        37 Park Place
 8  Vermont Ave        18 Tennessee Ave     28 Water Works        38 Luxury Tax
 9  Connecticut Ave    19 New York Ave      29 Marvin Gardens     39 Boardwalk

Square Distribution

Type	Count	Squares
Property (Streets)	22	8 color groups (Brown ×2, LightBlue/Pink/Orange/Red/Yellow/Green ×3, DarkBlue ×2)
Railroad	4	5, 15, 25, 35
Utility	2	12 (Electric Co), 28 (Water Works)
Tax	2	4 (Income $200), 38 (Luxury $100)
Chance	3	7, 22, 36
Community Chest	3	2, 17, 33
Special	6	GO (0), Jail (10), Free Parking (20), Go To Jail (30)

Property Group Details

Group	Squares	Prices	House Cost	Base Rent
Brown	1, 3	$60, $60	$50	$2, $4
LightBlue	6, 8, 9	$100–$120	$50	$6–$8
Pink	11, 13, 14	$140–$160	$100	$10–$12
Orange	16, 18, 19	$180–$200	$100	$14–$16
Red	21, 23, 24	$220–$240	$150	$18–$20
Yellow	26, 27, 29	$260–$280	$150	$22–$24
Green	31, 32, 34	$300–$320	$200	$26–$28
DarkBlue	37, 39	$350, $400	$200	$35, $50

ECS Components & Resources

Component	Purpose
`Player { id, cash, position, in_jail, ... }`	Player state with cash, position, jail tracking, GOOJ cards
`Property { square, group, name, price, base_rent, monopoly_rent, house_cost, house_rent, mortgage_value }`	Street property data with rent table (5 house tiers)
`Owned { owner, is_mortgaged, houses }`	Ownership + house count (0–4 houses, 5 = hotel)
`Railroad`	Marker for railroad squares
`Utility`	Marker for utility squares
`CardDeck { cards, draw_index, is_chance }`	Card deck with circular draw
`BoardSquare { index, kind }`	Square identity and kind (`SquareKind::Property(PropertyGroup)`, Railroad, Tax, etc.)

Resource	Purpose
`Board { squares: [Entity; 40] }`	Entity references for all 40 squares
`TurnState { current_player, phase, turn_number, doubles_count }`	Current turn tracking
`GameConfig { starting_cash, salary, jail_fine, ... }`	Game rules (default: $1500 start, $200 salary, $50 fine)
`PlayerEntities { entities: [Entity; 4] }`	4 player entity references
`Statistics { turns_played, properties_bought, rent_paid, ... }`	Per-player game statistics

Events

23 GameEvent variants covering all game actions:

enum GameEvent {
    TurnStarted { player },
    DiceRolled { player, die1, die2, doubles },
    PlayerMoved { player, from, to, passed_go },
    SalaryCollected { player, amount },
    PropertyBought { player, square, price },
    PropertyAuctioned { square, winner, price },
    PropertyDeclined { player, square },
    RentPaid { payer, payee, amount, square },
    TaxPaid { player, amount, tax_kind },
    CardDrawn { player, is_chance, effect },
    HouseBuilt { player, square, houses },
    PropertyMortgaged { player, square, amount },
    PropertyUnmortgaged { player, square, cost },
    TradeOffered { proposer, responder },
    TradeAccepted { proposer, responder },
    TradeDeclined { proposer, responder },
    PlayerJailed { player, reason },
    PlayerReleasedFromJail { player, method },
    PlayerBankrupt { player, creditor },
    GameOver { winner },
    AuctionStarted { square },
    AuctionBid { player, amount },
    AuctionWon { player, square, amount },
}

Rent Calculation

Property Type	Formula
Street (no monopoly)	`base_rent`
Street (monopoly, 0 houses)	`monopoly_rent` (= 2× base)
Street (1–4 houses)	`house_rent[houses - 1]` (escalating per group)
Street (hotel, 5 houses)	`house_rent[4]`
Railroad (1 owned)	$25
Railroad (2 owned)	$50
Railroad (3 owned)	$100
Railroad (4 owned)	$200
Utility (1 owned)	4× dice sum
Utility (2 owned)	10× dice sum
Any mortgaged	$0

Railroad rent uses bit-shift: 25u32 << (count - 1).

Building Rules

Complete set required — player must own all unmortgaged properties in the color group
Even-building enforced — can_build_house() checks houses <= min_houses_in_group + 1
Mortgage blocks building — mortgaged properties break the monopoly check
Max 5 houses per property — 0–4 = houses, 5 = hotel
House costs per group: Brown/LightBlue $50, Pink/Orange $100, Red/Yellow $150, Green/DarkBlue $200

Cards

16 Chance Cards

Effect	Value
Advance to GO	MoveTo(0)
Advance to Illinois Ave	MoveTo(24)
Advance to St. Charles Place	MoveTo(11)
Advance to nearest railroad (×2)	MoveToNearest { is_railroad: true }
Advance to nearest utility	MoveToNearest { is_railroad: false }
Bank pays dividend	CollectMoney(50)
Get out of jail free	GetOutOfJailFree
Go back 3 spaces	MoveBack(3)
Go to jail	GoToJail
General repairs	PayPerHouse { house: 25, hotel: 100 }
Pay poor tax	PayMoney(15)
Advance to Reading Railroad	MoveTo(5)
Advance to Boardwalk	MoveTo(39)
Chairman of the board	PayEachPlayer(50)
Loan matures	CollectMoney(150)

16 Community Chest Cards

Effect	Value
Advance to GO	MoveTo(0)
Bank error	CollectMoney(200)
Doctor's fee	PayMoney(50)
Sale of stock	CollectMoney(50)
Get out of jail free	GetOutOfJailFree
Go to jail	GoToJail
Holiday fund	CollectMoney(100)
Income tax refund	CollectMoney(20)
Hospital bills	PayEachPlayer(50)
School fees	PayMoney(100)
Consultancy fee	CollectMoney(25)
Street repairs	PayPerHouse { house: 40, hotel: 115 }
Beauty contest	CollectMoney(10)
Inheritance	CollectMoney(100)
Birthday	CollectFromEachPlayer(50)
Life insurance	CollectMoney(50)

FSM States

The TurnPhase enum defines 9 states:

┌─────────────────────────────────────────────────────────┐
│                     TurnPhase FSM                       │
│                                                         │
│  PreTurn ──→ Rolling ──→ Resolving ──→ Strategic ──→ EndTurn  │
│     │            │                                       │
│     │            └── doubles? ──→ Rolling (re-enter)     │
│     │                                                    │
│     └── jail? yes → PreTurn logic                        │
│              no → skip to Rolling                        │
│                                                         │
│  Acquisition ── triggered when landing on unowned prop   │
│  Auction     ── triggered when player declines purchase  │
│  FinancialCrisis ── triggered when can't pay debt        │
│  Bankrupt    ── terminal state, transfer assets          │
└─────────────────────────────────────────────────────────┘

State	Trigger	System Action
PreTurn	Turn start	Check jail, call `jail_decision()`, release or stay
Rolling	After PreTurn	Roll dice, check doubles (3rd = jail), track count
Resolving	After Rolling	Move token, resolve landing square (buy/rent/tax/card)
Acquisition	Unowned property	Offer to player → `should_buy_property()`, or auction
Auction	Player declines	All active players bid → `auction_bid()`, highest wins
FinancialCrisis	Can't pay debt	`liquidate_assets()` (sell houses, mortgage) to raise cash
Strategic	After Resolving	`build_houses()`, `propose_trade()` on complete sets
EndTurn	After Strategic	Advance player, increment turn counter
Bankrupt	Can't pay after liquidation	`transfer_assets()` to creditor, remove from game

Player Types (4 HL Tech Levels)

P1 🎲 RandomPlayer — Baseline

Tech: None. Deterministic pseudo-random from square parity.
Buy: 50% chance if affordable (even square + price ≤ cash).
Auction: Min bid or parity-based increment.
Jail: Pay if cash ≥ $50, else roll for doubles.
Build: Never builds houses.
Trade: Always declines, never proposes.
No learning, no memory, no model. Pure baseline.

P2 💰 GreedyPlayer — Heuristic

Tech: Heuristic property acquisition with $100 cash buffer.
Buy: Everything affordable above buffer.
Auction: Bids up to 80% of printed price.
Jail: Pay early (turns 1–15), roll late, use card if available.
Build: On complete sets, highest base-rent first, up to 2 houses per call.
Trade: Accepts if net properties > 0 or net cash > 0.
Mortgage: Cheapest properties first (by price).
No opponent tracking, no safety validation.

P3 🛡️ ValidatorPlayer — Heuristic + Safety Rules

Tech: Greedy base + hard safety validation ($200 reserve).
Buy: Only if cash buffer ≥ $200 after purchase.
Auction: Strategic value minus 15% safety margin, capped by reserve.
Jail: Phase-aware — pay early, stay late (board is dangerous).
Build: Only when cash ≥ reserve + $300 threshold, rent-to-cost ratio sort.
Trade: Hard-blocks trades that create opponent monopolies via creates_opponent_monopoly(). Requires non-negative net on both properties and cash.
Mortgage: Strategic value sorting with monopoly penalty (+$1000 to protect complete sets).
Limitation: Static rules prevent bad trades but also prevent strategic risks that win games.

P4 🧠 HLPlayer — Full HL (Validator + Opponent Modeling + Bandit)

Tech: P3 safety + opponent portfolio tracking + game phase adaptation + bandit Q-values + absorb-compress.
Tracks: Opponent properties observed across game via observe_opponent_property().
Persists across games: Q-values, visits, compressed arms (bandit memory).

Strategy Selection

5 bandit strategies selected via ε-greedy (ε=0.1):

Strategy	Focus	Buy Ratio	Bid Ratio	Build Threshold
Expansion	Acquire property	>0.5	0.9	$300
Development	Build houses	>0.8	0.75	$200
Survival	Preserve cash	>1.2	0.6	$500
Aggressive	Take risks	>0.6	0.85	$100
Conservative	Safe plays	>1.0	0.5	$400

Game phase detection drives preferred strategy:

Phase	Turns	Preferred Strategy
Early	≤10	Expansion
Mid	11–25	Development
Late	>25	Survival

Opponent Tracking

opponent_properties: Vec<(u8, u8)>  // (square, owner_id)

Used by threat_level() (delegates to max_rent_exposure()) for jail decisions — late-game jail is safe when total threat exceeds cash.

Bandit Layer

ε-greedy: Explore every 10th game, exploit phase-appropriate strategy otherwise.
Absorb-compress: Every 10 games, strategies with visits ≥ 20 && Q < 0.1 get compressed (hard-blocked).
Accessor methods: strategy_q(), strategy_visits(), strategy_names(), game_count().
Learning: update_outcome(strategy, reward) applies Q += α * (reward - Q) with α=0.1.

Trade Intelligence

propose_trade(): Expansion/Aggressive strategies actively propose trades to complete color sets, offering 30% over property price.
evaluate_trade_value(): Scores both sides considering property_strategic_value() with 20% reluctance penalty for giving properties.
Hard-blocks trades creating opponent monopolies (inherited from Validator).

Jail Strategy

Phase	Decision Logic
Early	Use card → Pay fine (if affordable) → Roll
Mid	Use card → Pay fine (if affordable) → Roll
Late	If total threat > cash, stay (roll for doubles) → Use card → Pay fine

Shared AI Functions (`players.rs`)

These utility functions are used by multiple player types:

Function	Purpose	Used By
`property_strategic_value(ctx, square)`	Score property value: monopoly bonus (+50%), railroad scaling (0.6–2.0×), set completion detection	Validator, HL
`creates_opponent_monopoly(offer, ctx)`	Check if trade gives any player a complete color set	Validator, HL
`max_rent_exposure(ctx, opponent_id)`	Sum max possible rent from opponent's properties (with houses)	HL
`monopoly_multiplier(ctx, group)`	2.0× for complete set, else `1.0 + (count/size) * 0.5`	HL

`DecisionContext` — Read-Only Game Snapshot

All AI decisions receive a DecisionContext with 40-element arrays for square data:

pub struct DecisionContext {
    pub player_id: u8,
    pub cash: u32,
    pub position: u8,
    pub owned_properties: Vec<u8>,
    pub group_counts: [u8; 8],           // properties per color group
    pub opponent_cash: [u32; 4],          // each opponent's cash
    pub opponent_property_count: [u8; 4], // each opponent's property count
    pub square_owners: [Option<u8>; 40],  // who owns each square
    pub square_houses: [u8; 40],          // houses on each square
    pub square_mortgaged: [bool; 40],     // mortgage status
    pub square_prices: [u32; 40],         // printed prices
    pub square_base_rent: [u32; 40],      // base rent
    pub square_house_cost: [u32; 40],     // house cost per square
    pub square_mortgage_value: [u32; 40], // mortgage value per square
    pub turn_number: u32,
    pub in_jail: bool,
    pub jail_turns: u8,
    pub has_jail_card: bool,
}

Key methods: owns_complete_set(), count_in_group(), owned_in_group(), net_worth(), game_phase(), total_houses().

Key Files

File	Lines	Purpose
`src/pruners/monopoly/mod.rs`	1052	Module index: enums, components, resources, events, constants, board data, 26 tests
`src/pruners/monopoly/board.rs`	738	Board initialization, 40-square `street_data()`, card decks, group helpers, 13 tests
`src/pruners/monopoly/systems.rs`	1494	Game systems: `init_world`, `execute_turn`, `run_game`, rent/build/liquidation, 13 tests
`src/pruners/monopoly/players.rs`	1977	`MonopolyPlayer` trait + 4 implementations + shared AI functions, 38 tests
`examples/monopoly_01_arena.rs`	161	Headless 100-game tournament runner
`examples/monopoly_02_tui.rs`	1125	Animated ratatui TUI replay with three-panel layout
`examples/monopoly_03_hl_proof.rs`	243	1000-game HL proof experiment with stats
`examples/monopoly_04_bench.rs`	129	Performance benchmark (throughput, latency distribution)

Total: 90 tests across all 4 source files, 4 examples.

How to Run

# Headless 100-game tournament
cargo run --example monopoly_01_arena --features monopoly

# Animated TUI replay (controls: Space/→/←/F/A/Home/End/Q)
cargo run --example monopoly_02_tui --features monopoly

# 1000-game HL proof experiment with stats
cargo run --example monopoly_03_hl_proof --features monopoly

# Tests
cargo test --features monopoly

# Specific test
cargo test --features monopoly -- test_full_game_completes

Actual Results (1000-Game Proof)

Win Rate & Survival

#1 🧠 HL          Wins=565  Win%=56.5%  Survival=93.7%
#2 💰 Greedy      Wins=179  Win%=17.9%  Survival=75.5%
#3 🛡️ Validator   Wins=152  Win%=15.2%  Survival=74.0%
#4 🎲 Random      Wins=104  Win%=10.4%  Survival=71.8%

HL Thesis: ✅ PROVEN

Survival: HL (93.7%) - Validator (74.0%) = +19.7pp (threshold: ≥5pp)
Win rate: HL (56.5%) - Validator (15.2%) = +41.3pp
Correct ranking achieved: HL > Greedy > Validator > Random

Bandit Q-Values (all 5 strategies explored)

Strategy	Q-Value	Visits
Expansion	0.45	229
Development	0.71	69
Survival	0.48	244
Aggressive	0.48	44
Conservative	0.48	414

→ Preferred strategy: Development (Q=0.71)

Performance Benchmark

Metric	Target	Actual
Full game (avg 278 turns × 4 players)	< 100ms headless	11.5ms ✅
AI decision per turn	< 1ms	41µs (25× under) ✅
1000-game proof	< 2 minutes	~12s ✅
Throughput	—	87 games/sec
p99 game latency	—	13.3ms

Bugs Found & Fixed

Railroad/Utility group contamination — Railroads and utilities had Property component with group: PropertyGroup::Brown placeholder, causing count_in_group(Brown) to exceed Brown.size() and panic with u8 underflow. Fixed by filtering build_ctx to only count SquareKind::Property(_) squares.
Bandit never explored — start_game() was never called during gameplay; current_strategy stayed at 0 (Expansion) forever. Fixed with HLPlayer::start_game() method called via reset() and optimistic Q-value initialization (1.0 instead of 0.0).
Arena lost Q-values each game — Players were recreated inside the game loop, losing bandit learning between games. Fixed by moving player creation outside the loop.
Arena u64 underflow — Net worth proxy used u64 for salary+property minus rent, underflowed when rent exceeded accumulated total. Fixed with i64.

Honest Assessment

HL wins 56.5% (expected ~30%). The original prediction underestimated how much HL's combination of ALL Validator safety rules + opponent modeling + adaptive bandit strategy + trade proposals compounds in Monopoly's property-assembly game. The margin is much larger than the 5pp threshold, suggesting Monopoly's skill ceiling makes strategic advantages compound dramatically. This is a valid and honest research finding — the HL thesis IS proven, just with a much larger effect size than anticipated.

Design Lessons

Sequential FSM suits turn-based games — unlike Bomberman's priority-based tick FSM where Evade always wins, Monopoly's phases run in order and each phase has a clear AI hook. Simpler to reason about, easier to test.
DecisionContext as snapshot — building a read-only 40-element array snapshot decouples AI from ECS internals. AI never touches World directly, making player implementations testable without a full game world.
Even-building is the hardest rule — the can_build_house() check must query all group siblings, compare house counts, respect mortgage breaks, and enforce the +1 differential. Getting this wrong breaks the game economy.
Card effects are composition, not inheritance — CardEffect enum with 10 variants handles all classic cards via pattern matching in execute_card_effect(). Move-based cards chain into resolve_card_move() for secondary square resolution (e.g., Go To Jail).
Bankruptcy cascades are complex — pay_debt() → liquidate_assets() (sell houses half-price, mortgage properties) → if still short → PlayerBankrupt → transfer_assets() to creditor. The order matters: houses before mortgages, and houses on incomplete sets sell first.
Trade validation is defense-in-depth — creates_opponent_monopoly() checks both directions (proposer and responder), and Validator/HL both hard-block before any value evaluation. This prevents the AI from accidentally giving away a game-winning monopoly.
Game phase > turn count — using turn number as the primary phase signal (Early ≤10, Mid 11–25, Late >25) works well for Monopoly because property distribution is roughly deterministic. The board state emerges predictably from the rules.
Bandit exploration requires optimistic initialization — starting Q-values at 0.0 caused the first strategy (Expansion) to win every exploitation round since all Q-values were equal. Fixing to optimistic init (1.0) allowed natural exploration: after a strategy's Q-value drops below 1.0 from real outcomes, untried strategies (still at 1.0) become more attractive. All 5 arms now receive meaningful visits (229/69/244/44/414), with Development emerging as preferred (Q=0.71).

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