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Nikolaj
@@ -13,19 +13,17 @@ logger = logging.getLogger("app")
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AI_USER_ID = "ai"
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class AIPersonality(Enum):
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AGGRESSIVE = "aggressive" # Prefers high attack cards, plays aggressively
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DEFENSIVE = "defensive" # Prefers high defense cards, plays conservatively
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BALANCED = "balanced" # Mix of offense and defense
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GREEDY = "greedy" # Prioritizes high cost cards, willing to sacrifice
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SWARM = "swarm" # Prefers low cost cards, fills board quickly
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CONTROL = "control" # Focuses on board control and efficiency
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ARBITRARY = "arbitrary" # Just does whatever
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JEBRASKA = "jebraska" # Trained neural network plan scorer
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AGGRESSIVE = "aggressive"
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DEFENSIVE = "defensive"
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BALANCED = "balanced"
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GREEDY = "greedy" # prioritizes high cost cards, willing to sacrifice
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SWARM = "swarm"
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CONTROL = "control"
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ARBITRARY = "arbitrary"
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JEBRASKA = "jebraska" # trained neural network plan scorer
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def get_random_personality() -> AIPersonality:
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"""Returns a random AI personality."""
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# return random.choice(list(AIPersonality))
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return AIPersonality.JEBRASKA
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return random.choice(list(AIPersonality))
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def calculate_exact_cost(attack: int, defense: int) -> float:
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"""Calculate the exact cost before rounding (matches card.py formula)."""
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@@ -130,8 +128,6 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality)
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return selected
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# ==================== Turn planning ====================
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@dataclass
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class MovePlan:
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sacrifice_slots: list[int]
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@@ -175,7 +171,6 @@ def _plans_for_sacrifice(player, opponent, sacrifice_slots):
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def generate_plans(player, opponent) -> list[MovePlan]:
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"""Generate diverse candidate move plans covering a range of strategies."""
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plans = []
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# Sacrifice n board cards
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@@ -189,8 +184,6 @@ def generate_plans(player, opponent) -> list[MovePlan]:
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return plans
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# ==================== Turn execution ====================
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def score_plans_batch(
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plans: list[MovePlan],
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player: PlayerState,
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@@ -205,7 +198,7 @@ def score_plans_batch(
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for c in player.hand
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}
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# Build board-state arrays with one Python loop (unavoidable)
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# Build board-state arrays
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board_atk = np.zeros((n, BOARD_SIZE), dtype=np.float32)
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board_occ = np.zeros((n, BOARD_SIZE), dtype=np.bool_)
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n_sac = np.zeros(n, dtype=np.float32)
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@@ -390,7 +383,6 @@ async def run_ai_turn(game_id: str):
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except Exception:
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pass
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# --- Generate and score candidate plans ---
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best_plan = choose_plan(player, opponent, personality, difficulty)
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logger.info(
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@@ -398,7 +390,6 @@ async def run_ai_turn(game_id: str):
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f"sac={best_plan.sacrifice_slots} plays={[c.name for c, _ in best_plan.plays]}"
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)
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# --- Execute sacrifices ---
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for slot in best_plan.sacrifice_slots:
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card_slot = player.board[slot]
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if card_slot is None:
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@@ -409,7 +400,6 @@ async def run_ai_turn(game_id: str):
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await send_state(state)
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await asyncio.sleep(0.35)
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# --- Execute plays ---
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# Shuffle play order so the AI doesn't always fill slots left-to-right
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plays = list(best_plan.plays)
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random.shuffle(plays)
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@@ -132,8 +132,6 @@ class NeuralNet:
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return net
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# ==================== Feature extraction ====================
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def extract_plan_features(plans: list, player, opponent) -> np.ndarray:
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"""
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Returns (n_plans, N_FEATURES) float32 array.
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@@ -143,7 +141,7 @@ def extract_plan_features(plans: list, player, opponent) -> np.ndarray:
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n = len(plans)
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# ---- state (same for every plan) ----
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# state (same for every plan)
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state = np.array([
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player.life / STARTING_LIFE,
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opponent.life / STARTING_LIFE,
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@@ -155,7 +153,7 @@ def extract_plan_features(plans: list, player, opponent) -> np.ndarray:
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len(opponent.deck) / _MAX_DECK,
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], dtype=np.float32)
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# ---- current boards (same for every plan) ----
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# current boards (same for every plan)
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my_board = np.zeros(BOARD_SIZE * 3, dtype=np.float32)
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opp_board = np.zeros(BOARD_SIZE * 3, dtype=np.float32)
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for slot in range(BOARD_SIZE):
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@@ -170,7 +168,7 @@ def extract_plan_features(plans: list, player, opponent) -> np.ndarray:
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opp_board[slot * 3 + 1] = c.defense / _MAX_DEF
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opp_board[slot * 3 + 2] = 1.0
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# ---- per-plan features ----
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# per-plan features
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plan_part = np.zeros((n, 3 + BOARD_SIZE * 3), dtype=np.float32)
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for idx, plan in enumerate(plans):
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# simulate board result
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@@ -192,7 +190,7 @@ def extract_plan_features(plans: list, player, opponent) -> np.ndarray:
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plan_part[idx, 3 + slot * 3 + 1] = c.defense / _MAX_DEF
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plan_part[idx, 3 + slot * 3 + 2] = 1.0
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# ---- opponent deck type one-hot (same for every plan) ----
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# opponent deck type one-hot (same for every plan)
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opp_deck_oh = np.zeros(len(_DECK_TYPES), dtype=np.float32)
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opp_deck_oh[_DECK_TYPE_IDX.get(opponent.deck_type, 0)] = 1.0
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@@ -204,8 +202,6 @@ def extract_plan_features(plans: list, player, opponent) -> np.ndarray:
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return np.concatenate([state_t, my_board_t, opp_board_t, plan_part, opp_deck_t], axis=1)
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# ==================== Neural player ====================
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class NeuralPlayer:
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"""
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Wraps a NeuralNet for use in game simulation.
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@@ -21,8 +21,6 @@ SIMULATION_CARDS_PATH = os.path.join(os.path.dirname(__file__), "simulation_card
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SIMULATION_CARD_COUNT = 1000
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# ==================== Card pool ====================
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def _card_to_dict(card: Card) -> dict:
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return {
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"name": card.name,
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@@ -69,8 +67,6 @@ def get_simulation_cards() -> list[Card]:
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return cards
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# ==================== Single game ====================
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PLAYER1_ID = "p1"
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PLAYER2_ID = "p2"
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MAX_TURNS = 300 # safety cap to prevent infinite games
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@@ -176,7 +172,6 @@ def simulate_game(
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return None
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# ==================== Process-pool worker ====================
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# These must be module-level so they are picklable.
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_worker_cards: list[Card] = []
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@@ -186,7 +181,6 @@ def _init_worker(cards: list[Card]) -> None:
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_worker_cards = cards
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def _run_game_sync(args: tuple) -> str | None:
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"""Synchronous entry point for a worker process."""
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d1, p1_name, d2, p2_name = args
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return simulate_game(
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_worker_cards,
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@@ -195,8 +189,6 @@ def _run_game_sync(args: tuple) -> str | None:
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)
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# ==================== Tournament ====================
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def _all_players(difficulties: list[int] | None = None) -> list[tuple[AIPersonality, int]]:
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"""Return all (personality, difficulty) combinations for the given difficulties (default 1-10)."""
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if difficulties is None:
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@@ -232,7 +224,6 @@ async def run_tournament(
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players = _all_players(difficulties)
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n = len(players)
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# Build the flat list of (i, j, args) for every game
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indexed_args: list[tuple[int, int, tuple]] = []
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for i in range(n):
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p1_personality, p1_difficulty = players[i]
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@@ -20,8 +20,6 @@ P2 = "p2"
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FIXED_PERSONALITIES = [p for p in AIPersonality if p != AIPersonality.ARBITRARY]
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# ==================== Game runner ====================
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def _build_player(pid: str, name: str, cards: list, difficulty: int, personality: AIPersonality) -> PlayerState:
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deck = choose_cards(cards, difficulty, personality)
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instances = _make_instances(deck)
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@@ -82,8 +80,6 @@ def run_episode(
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return state.result.winner_id if state.result else None
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# ==================== Training loop ====================
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def train(
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n_episodes: int = 20_000,
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self_play_start: int = 5_000,
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@@ -124,7 +120,6 @@ def train(
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nn_goes_first = random.random() < 0.5
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if random.random() < self_play_prob:
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# ---- Self-play ----
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nn1 = NeuralPlayer(net, training=True, temperature=temperature)
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nn2 = NeuralPlayer(net, training=True, temperature=temperature)
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@@ -148,7 +143,6 @@ def train(
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batch_count += 1
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else:
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# ---- NN vs fixed opponent ----
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opp_personality = random.choice(FIXED_PERSONALITIES)
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nn_player = NeuralPlayer(net, training=True, temperature=temperature)
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opp_ctrl = lambda p, o, pers=opp_personality, diff=opp_difficulty: choose_plan(p, o, pers, diff)
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@@ -199,7 +199,7 @@
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<div class="section-divider"></div>
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<div class="specific-section">
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<div class="specific-card-preview">
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<div class="specific-card-preview" class:cannot-afford={shards !== null && shards < SPECIFIC_CARD_COST}>
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<div class="specific-card-row">
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<div class="specific-card-back">
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<div class="specific-back-logo"></div>
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@@ -318,7 +318,7 @@
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height: 100vh;
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overflow-y: auto;
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background: #0d0a04;
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padding: 2.5rem 2rem;
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padding: 2.5rem 2rem 5rem;
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display: flex;
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flex-direction: column;
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align-items: center;
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@@ -416,7 +416,8 @@
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background: #211408;
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}
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.pkg-card.cannot-afford {
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.pkg-card.cannot-afford,
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.specific-card-preview.cannot-afford {
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opacity: 0.5;
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}
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