🐐

.gitignore

@@ -2,3 +2,6 @@
 __pycache__/
 .svelte-kit/
 .env
+
+backend/simulation_cards.json
+backend/tournament_grid.png

backend/ai.py

@@ -3,9 +3,10 @@ import random
 import logging
 from dataclasses import dataclass
 from enum import Enum
-from itertools import combinations
+from itertools import combinations, permutations
+import numpy as np
 from card import Card
-from game import action_play_card, action_sacrifice, action_end_turn, BOARD_SIZE, STARTING_LIFE
+from game import action_play_card, action_sacrifice, action_end_turn, BOARD_SIZE, STARTING_LIFE, PlayerState
 
 logger = logging.getLogger("app")
 
@@ -18,6 +19,7 @@ class AIPersonality(Enum):
   GREEDY = "greedy"           # Prioritizes high cost cards, willing to sacrifice
   SWARM = "swarm"             # Prefers low cost cards, fills board quickly
   CONTROL = "control"         # Focuses on board control and efficiency
+  SHOCKER = "shocker"         # Cheap high-defense walls + a few powerful high-attack finishers
   ARBITRARY = "arbitrary"     # Just does whatever
 
 def get_random_personality() -> AIPersonality:
@@ -40,78 +42,70 @@ def get_power_curve_value(card) -> float:
 def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality) -> list[Card]:
   BUDGET = 50
 
-  logger.info(f"Personality: {personality.value}")
-  logger.info(f"Difficulty: {difficulty}")
-  card_strings = [
-    f"{c.name} {c.cost}"
-    for c in sorted(cards, key=lambda x: x.cost)[::-1][:20]
-  ]
-  logger.info("Cards:\n"+("\n".join(card_strings)))
-
-  # God cards (cost 7-11) are gated by difficulty. Below difficulty 7 they are excluded.
-  # Each level from 7 upward unlocks a higher cost tier; at difficulty 10 all are allowed.
   if difficulty >= 6:
-    max_card_cost = difficulty+1
+    max_card_cost = difficulty + 1
   else:
     max_card_cost = 6
 
   allowed = [c for c in cards if c.cost <= max_card_cost] or list(cards)
 
-  def card_score(card: Card) -> float:
+  # Vectorized scoring over all allowed cards at once
-    pcv = get_power_curve_value(card)
+  atk  = np.array([c.attack  for c in allowed], dtype=np.float32)
-    # Normalize pcv to [0, 1].
+  defn = np.array([c.defense for c in allowed], dtype=np.float32)
-    pcv_norm = max(0.0, min(1.0, pcv))
+  cost = np.array([c.cost    for c in allowed], dtype=np.float32)
 
-    cost_norm = card.cost / max_card_cost   # [0, 1]; higher = more expensive
+  exact_cost    = np.minimum(11.0, np.maximum(1.0, ((atk**2 + defn**2)**0.18) / 1.5))
-    total = card.attack + card.defense
+  pcv_norm      = np.clip(exact_cost - cost, 0.0, 1.0)
-    atk_ratio = card.attack / total if total else 0.5
+  cost_norm     = cost / max_card_cost
+  totals        = atk + defn
+  atk_ratio     = np.where(totals > 0, atk / totals, 0.5)
+  def_not_one   = np.where(defn != 1, 1.0, 0.0)
 
   if personality == AIPersonality.AGGRESSIVE:
-      # Prefers high-attack cards; slight bias toward high cost for raw power
+    # (1-cost_norm) penalizes expensive cards. High-attack cards are inherently expensive,
-      return 0.50 * atk_ratio + 0.30 * pcv_norm + 0.20 * cost_norm
+    # so without this the second pass drifts toward costly cards at higher difficulty,
+    # shrinking the deck. The bonus grows with max_card_cost and exactly offsets that drift.
+    scores = 0.50 * atk_ratio + 0.35 * pcv_norm + 0.15 * (1.0 - cost_norm) + 0.10 * def_not_one
+  elif personality == AIPersonality.DEFENSIVE:
+    # Small (1-cost_norm) for the same anti-shrinkage reason; lighter because high-defense
+    # cards don't correlate as strongly with cost as high-attack cards do.
+    scores = 0.10 * (1.0 - atk_ratio) + 0.80 * pcv_norm + 0.10 * cost_norm
+  elif personality == AIPersonality.GREEDY:
+    # Small cost_norm keeps flavour without causing severe deck shrinkage at D10
+    scores = 0.20 * cost_norm + 0.80 * pcv_norm
+  elif personality == AIPersonality.SWARM:
+    scores = 0.40 * (1.0 - cost_norm) + 0.35 * atk_ratio + 0.20 * pcv_norm + 0.05 * def_not_one
+  elif personality == AIPersonality.CONTROL:
+    # Small cost_norm keeps flavour without causing severe deck shrinkage at D10
+    scores = 0.85 * pcv_norm + 0.15 * cost_norm
+  elif personality == AIPersonality.BALANCED:
+    scores = 0.60 * pcv_norm + 0.25 * atk_ratio + 0.15 * (1.0 - atk_ratio)
+  elif personality == AIPersonality.SHOCKER:
+    # Both cheap walls and expensive finishers want high attack.
+    # (1-cost_norm) drives first-pass cheap-card selection; pcv_norm drives second-pass finishers.
+    # defense_ok zeros out cards with defense==1 on the first term so fragile walls are excluded.
+    # cost-11 cards have pcv=0 so they score near-zero and never shrink the deck.
+    scores = atk_ratio * (1.0 - cost_norm) * def_not_one + atk_ratio * pcv_norm
+  else:  # ARBITRARY
+    w = 0.05 * difficulty
+    scores = w * pcv_norm + (1.0 - w) * np.random.random(len(allowed)).astype(np.float32)
 
-    if personality == AIPersonality.DEFENSIVE:
+  # Small noise floor at D10 prevents fully deterministic deck building.
-      # Prefers high-defense cards; same cost bias
+  # A locked-in deck loses every game against counters; tiny randomness avoids this.
-      return 0.50 * (1.0 - atk_ratio) + 0.30 * pcv_norm + 0.20 * cost_norm
+  noise = max(0.03, (10 - difficulty) / 9.0) * 0.50
+  scores = scores + np.random.normal(0, noise, len(allowed)).astype(np.float32)
 
-    if personality == AIPersonality.GREEDY:
+  order = np.argsort(-scores)
-      # Fills budget with the fewest, most expensive cards possible
+  sorted_cards = [allowed[i] for i in order]
-      return 0.70 * cost_norm + 0.30 * pcv_norm
 
-    if personality == AIPersonality.SWARM:
-      # Cheap cards
-      return 0.45 * (1.0 - cost_norm) + 0.35 * atk_ratio + 0.20 * pcv_norm
-
-    if personality == AIPersonality.CONTROL:
-      # Values efficiency above all: wants cards that are above the power curve,
-      # with a secondary preference for higher cost
-      return 0.70 * pcv_norm + 0.30 * cost_norm
-
-    if personality == AIPersonality.BALANCED:
-      # Blends everything: efficiency, cost spread, and a slight attack lean
-      return 0.40 * pcv_norm + 0.35 * cost_norm + 0.15 * atk_ratio + 0.10 * (1.0 - atk_ratio)
-
-    # ARBITRARY: mostly random at lower difficulties
-    return (0.05 * difficulty) * pcv_norm + (1 - (0.05 * difficulty)) * random.random()
-
-  # Higher difficulty -> less noise -> more optimal deck composition
-  noise = ((10 - difficulty) / 9.0) * 0.50
-
-  scored = sorted(
-    [(card_score(c) + random.gauss(0, noise), c) for c in allowed],
-    key=lambda x: x[0],
-    reverse=True,
-  )
-
-  # Minimum budget reserved for cheap (cost 1-3) cards to ensure early-game presence.
-  # Without cheap cards the AI will play nothing for the first several turns.
   early_budget = {
-    AIPersonality.GREEDY:      4,
+    AIPersonality.GREEDY:     20,  # cheap cards are sacrifice fodder for big plays
     AIPersonality.SWARM:      12,
-    AIPersonality.AGGRESSIVE:  8,
+    AIPersonality.AGGRESSIVE: 18,  # raised: ensures cheap high-attack fodder regardless of difficulty
-    AIPersonality.DEFENSIVE:  10,
+    AIPersonality.DEFENSIVE:  15,  # raised: stable cheap-card base across difficulty levels
     AIPersonality.CONTROL:     8,
-    AIPersonality.BALANCED:   10,
+    AIPersonality.BALANCED:   25,  # spread the deck across all cost levels
+    AIPersonality.SHOCKER:    15,  # ~15 cost-1 shields, then expensive attackers fill remaining budget
     AIPersonality.ARBITRARY:   8,
   }[personality]
 
@@ -120,7 +114,7 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality)
 
   # First pass: secure early-game cards
   cheap_spent = 0
-  for _, card in scored:
+  for card in sorted_cards:
     if cheap_spent >= early_budget:
       break
     if card.cost > 3 or total_cost + card.cost > BUDGET:
@@ -131,7 +125,7 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality)
 
   # Second pass: fill remaining budget greedily by score
   taken = {id(c) for c in selected}
-  for _, card in scored:
+  for card in sorted_cards:
     if total_cost >= BUDGET:
       break
     if id(card) in taken or total_cost + card.cost > BUDGET:
@@ -139,13 +133,6 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality)
     selected.append(card)
     total_cost += card.cost
 
-
-  card_strings = [
-    f"{c.name} {c.cost}"
-    for c in sorted(selected, key=lambda x: x.cost)
-  ]
-  logger.info("Selected:\n"+("\n".join(card_strings)))
-
   return selected
 
 
@@ -182,13 +169,6 @@ def _plans_for_sacrifice(player, opponent, sacrifice_slots):
   empty_slots = [i for i, c in enumerate(board) if c is None]
   en_board = opponent.board
 
-  # For scoring: open enemy slots first so the simulation reflects
-  # direct-damage potential accurately.
-  scoring_slots = (
-    [s for s in empty_slots if en_board[s] is None] +
-    [s for s in empty_slots if en_board[s] is not None]
-  )
-
   return [
     MovePlan(
       sacrifice_slots=list(sacrifice_slots),
@@ -196,6 +176,7 @@ def _plans_for_sacrifice(player, opponent, sacrifice_slots):
       label=f"sac{len(sacrifice_slots)}_play{len(cards)}",
     )
     for cards in _affordable_subsets(hand, energy)
+    for scoring_slots in permutations(empty_slots, len(cards))
   ]
 
 
@@ -214,230 +195,152 @@ def generate_plans(player, opponent) -> list[MovePlan]:
 
   return plans
 
+# ==================== Turn execution ====================
 
-def score_plan(plan: MovePlan, player, opponent, personality: AIPersonality) -> float:
+def score_plans_batch(
-  """
+  plans: list[MovePlan],
-  Score a plan from ~0.0 to ~1.0 based on the projected board state after
+  player: PlayerState,
-  executing it. Higher is better.
+  opponent: PlayerState,
-  """
+  personality: AIPersonality,
-  # Simulate board after sacrifices + plays
+) -> np.ndarray:
+  n = len(plans)
+
+  # Pre-compute PCV for every hand card once
+  pcv_cache = {
+    id(c): max(0.0, min(1.0, get_power_curve_value(c)))
+    for c in player.hand
+  }
+
+  # Build board-state arrays with one Python loop (unavoidable)
+  board_atk = np.zeros((n, BOARD_SIZE), dtype=np.float32)
+  board_occ = np.zeros((n, BOARD_SIZE), dtype=np.bool_)
+  n_sac     = np.zeros(n, dtype=np.float32)
+  sac_val   = np.zeros(n, dtype=np.float32)
+  play_val  = np.zeros(n, dtype=np.float32)
+  pcv_score = np.full(n, 0.5, dtype=np.float32)
+
+  for idx, plan in enumerate(plans):
     board = list(player.board)
-  energy = player.energy
     for slot in plan.sacrifice_slots:
-    if board[slot] is not None:
+      board_slot = board[slot]
+      if board_slot is not None:
+        sac_val[idx] += board_slot.cost
         board[slot] = None
-      energy += 1
+    n_sac[idx] = len(plan.sacrifice_slots)
     for card, slot in plan.plays:
       board[slot] = card
-
+      play_val[idx] += card.cost
-  en_board = opponent.board
-  enemy_occupied = sum(1 for c in en_board if c is not None)
-
-  # --- Combat metrics ---
-  direct_damage = 0      # AI attacks going straight to opponent life
-  board_damage = 0       # AI attacks hitting enemy cards
-  blocking_slots = 0     # Slots where AI blocks an enemy card
-  cards_destroyed = 0    # Enemy cards the AI would destroy this turn
-  unblocked_incoming = 0 # Enemy attacks that go straight to AI life
-  cards_on_board = 0
-
     for slot in range(BOARD_SIZE):
-    my = board[slot]
+      board_slot = board[slot]
-    en = en_board[slot]
+      if board_slot is not None:
-    if my:
+        board_atk[idx, slot] = board_slot.attack
-      cards_on_board += 1
+        board_occ[idx, slot] = True
-    if my and en is None:
-      direct_damage += my.attack
-    if my and en:
-      board_damage += my.attack
-      blocking_slots += 1
-      if my.attack >= en.defense:
-        cards_destroyed += 1
-    if not my and en:
-      unblocked_incoming += en.attack
-
-  # --- Normalize to [0, 1] ---
-  # How threatening is the attack relative to what remains of opponent's life?
-  atk_score = min(1.0, direct_damage / max(opponent.life, 1))
-
-  # What fraction of enemy slots are blocked?
-  block_score = (blocking_slots / enemy_occupied) if enemy_occupied > 0 else 1.0
-
-  # What fraction of all slots are filled?
-  cover_score = cards_on_board / BOARD_SIZE
-
-  # What fraction of enemy cards do are destroyed?
-  destroy_score = (cards_destroyed / enemy_occupied) if enemy_occupied > 0 else 0.0
-
-  # How safe is the AI from unblocked hits relative to its own life?
-  threat_score = 1.0 - min(1.0, unblocked_incoming / max(player.life, 1))
-
-  # How many cards compared to the enemy?
-  opponent_cards_left = len(opponent.deck) + len(opponent.hand) + enemy_occupied
-  my_cards_left = len(player.deck) + len(player.hand) + blocking_slots
-  attrition_score = my_cards_left/(my_cards_left + opponent_cards_left)
-
-  # Net value: cost of cards played minus cost of cards sacrificed.
-  n_sac = len(plan.sacrifice_slots)
-  sac_value = sum(player.board[s].cost for s in plan.sacrifice_slots if player.board[s] is not None)
-  play_value = sum(c.cost for c, _ in plan.plays)
-  net_value = play_value - sac_value
-  net_value_norm = max(0.0, min(1.0, (net_value + 10) / 20))
-
-  # Sacrifice penalty. Applied as a flat deduction after personality scoring.
-  sacrifice_penalty = 0.0
-  if n_sac > 0:
-    # Penalty 1: wasted energy. Each sacrifice gives +1 energy; if that energy
-    # goes unspent it was pointless. Weighted heavily.
-    energy_leftover = player.energy + n_sac - play_value
-    wasted_sac_energy = max(0, min(n_sac, energy_leftover))
-    wasted_penalty = wasted_sac_energy / n_sac
-
-    # Penalty 2: low-value swap. Each sacrifice should at minimum unlock a card
-    # that costs more than the one removed (net_value > n_sac means each
-    # sacrifice bought at least one extra cost point). Anything less is a bad trade.
-    swap_penalty = max(0.0, min(1.0, (n_sac - net_value) / max(n_sac, 1)))
-
-    sacrifice_penalty = 0.65 * wasted_penalty + 0.35 * swap_penalty
-
-  # Power curve value of the cards played (are they good value for their cost?)
     if plan.plays:
-    pcv_scores = [max(0.0, min(1.0, get_power_curve_value(c))) for c, _ in plan.plays]
+      pcv_vals = [pcv_cache.get(id(c), 0.5) for c, _ in plan.plays]
-    pcv_score = sum(pcv_scores) / len(pcv_scores)
+      pcv_score[idx] = sum(pcv_vals) / len(pcv_vals)
-  else:
+
-    pcv_score = 0.5
+  # Enemy board — same for every plan
+  en_atk = np.array([c.attack   if c else 0 for c in opponent.board], dtype=np.float32)
+  en_def = np.array([c.defense  if c else 0 for c in opponent.board], dtype=np.float32)
+  en_occ = np.array([c is not None          for c in opponent.board], dtype=np.bool_)
+  enemy_occupied = int(en_occ.sum())
+
+  # --- Metrics (all shape (n,)) ---
+  direct_damage     = (board_atk *  ~en_occ).sum(axis=1)
+  blocking          = board_occ & en_occ                          # (n, 5)
+  blocking_slots    = blocking.sum(axis=1).astype(np.float32)
+  cards_on_board    = board_occ.sum(axis=1).astype(np.float32)
+  cards_destroyed   = ((board_atk >= en_def) & blocking).sum(axis=1).astype(np.float32)
+  unblocked_in      = (en_atk * ~board_occ).sum(axis=1)
+
+  atk_score     = np.minimum(1.0, direct_damage / max(opponent.life, 1))
+  block_score   = blocking_slots / enemy_occupied if enemy_occupied > 0 else np.ones(n,  dtype=np.float32)
+  open_slots    = BOARD_SIZE - enemy_occupied
+  cover_score   = (
+    (cards_on_board - blocking_slots) / open_slots
+    if open_slots > 0
+    else np.ones(n, dtype=np.float32)
+  )
+  destroy_score = cards_destroyed / enemy_occupied if enemy_occupied > 0 else np.zeros(n, dtype=np.float32)
+  threat_score  = 1.0 - np.minimum(1.0, unblocked_in / max(player.life, 1))
+
+  opp_cards_left = len(opponent.deck) + len(opponent.hand) + enemy_occupied
+  my_cards_left  = len(player.deck) + len(player.hand) + blocking_slots
+  attrition_score = my_cards_left / (my_cards_left + max(opp_cards_left, 1))
+
+  net_value      = play_val - sac_val
+  net_value_norm = np.clip((net_value + 10) / 20, 0.0, 1.0)
+
+  # --- Sacrifice penalty ---
+  energy_leftover = player.energy + n_sac - play_val
+  wasted_energy   = np.maximum(0, np.minimum(n_sac, energy_leftover))
+  wasted_penalty  = np.where(n_sac > 0, wasted_energy / np.maximum(n_sac, 1), 0.0)
+  swap_penalty    = np.clip((n_sac - net_value) / np.maximum(n_sac, 1), 0.0, 1.0)
+  sac_penalty     = np.where(n_sac > 0, 0.65 * wasted_penalty + 0.35 * swap_penalty, 0.0)
 
   # --- Personality weights ---
   if personality == AIPersonality.AGGRESSIVE:
-    # Maximize direct damage
+    score = (0.30 * atk_score + 0.07 * block_score + 0.15 * cover_score +
-    score = (
+             0.08 * net_value_norm + 0.25 * destroy_score +
-      0.40 * atk_score +
+             0.08 * attrition_score + 0.04 * pcv_score + 0.03 * threat_score)
-      0.10 * block_score +
-      0.10 * cover_score +
-      0.10 * net_value_norm +
-      0.15 * destroy_score +
-      0.05 * attrition_score +
-      0.05 * pcv_score +
-      0.05 * threat_score
-    )
-
   elif personality == AIPersonality.DEFENSIVE:
-    # Block everything
+    score = (0.12 * atk_score + 0.20 * block_score + 0.18 * cover_score +
-    score = (
+             0.04 * net_value_norm + 0.18 * destroy_score +
-      0.05 * atk_score +
+             0.15 * attrition_score + 0.05 * pcv_score + 0.08 * threat_score)
-      0.35 * block_score +
-      0.20 * cover_score +
-      0.05 * net_value_norm +
-      0.05 * destroy_score +
-      0.10 * attrition_score +
-      0.05 * pcv_score +
-      0.15 * threat_score
-    )
-
   elif personality == AIPersonality.SWARM:
-    # Fill the board and press with direct damage
+    score = (0.25 * atk_score + 0.10 * block_score + 0.35 * cover_score +
-    score = (
+             0.05 * net_value_norm + 0.05 * destroy_score +
-      0.25 * atk_score +
+             0.10 * attrition_score + 0.05 * pcv_score + 0.05 * threat_score)
-      0.10 * block_score +
-      0.35 * cover_score +
-      0.05 * net_value_norm +
-      0.05 * destroy_score +
-      0.10 * attrition_score +
-      0.05 * pcv_score +
-      0.05 * threat_score
-    )
-
   elif personality == AIPersonality.GREEDY:
-    # High-value card plays, willing to sacrifice weak cards for strong ones
+    score = (0.15 * atk_score + 0.05 * block_score + 0.18 * cover_score +
-    score = (
+             0.38 * net_value_norm + 0.05 * destroy_score +
-      0.20 * atk_score +
+             0.09 * attrition_score + 0.05 * pcv_score + 0.05 * threat_score)
-      0.05 * block_score +
-      0.10 * cover_score +
-      0.40 * net_value_norm +
-      0.05 * destroy_score +
-      0.05 * attrition_score +
-      0.10 * pcv_score +
-      0.05 * threat_score
-    )
-
   elif personality == AIPersonality.CONTROL:
-    # Efficiency
+    score = (0.10 * atk_score + 0.05 * block_score + 0.05 * cover_score +
-    score = (
+             0.20 * net_value_norm + 0.05 * destroy_score +
-      0.10 * atk_score +
+             0.10 * attrition_score + 0.40 * pcv_score + 0.05 * threat_score)
-      0.05 * block_score +
-      0.05 * cover_score +
-      0.20 * net_value_norm +
-      0.05 * destroy_score +
-      0.10 * attrition_score +
-      0.40 * pcv_score +
-      0.05 * threat_score
-    )
-
   elif personality == AIPersonality.BALANCED:
-    score = (
+    score = (0.12 * atk_score + 0.13 * block_score + 0.15 * cover_score +
-      0.10 * atk_score +
+             0.10 * net_value_norm + 0.12 * destroy_score +
-      0.15 * block_score +
+             0.15 * attrition_score + 0.12 * pcv_score + 0.11 * threat_score)
-      0.10 * cover_score +
+  elif personality == AIPersonality.SHOCKER:
-      0.10 * net_value_norm +
+    score = (0.25 * destroy_score + 0.33 * cover_score + 0.18 * atk_score +
-      0.10 * destroy_score +
+             0.05 * block_score + 0.8 * attrition_score + 0.02 * threat_score +
-      0.10 * attrition_score +
+             0.05 * net_value_norm + 0.04 * pcv_score)
-      0.15 * pcv_score +
-      0.10 * threat_score
-    )
-
   else:  # ARBITRARY
-    score = (
+    score = (0.60 * np.random.random(n).astype(np.float32) +
-      0.60 * random.random() +
+             0.05 * atk_score + 0.05 * block_score + 0.05 * cover_score +
-      0.05 * atk_score +
+             0.05 * net_value_norm + 0.05 * destroy_score +
-      0.05 * block_score +
+             0.05 * attrition_score + 0.05 * pcv_score + 0.05 * threat_score)
-      0.05 * cover_score +
-      0.05 * net_value_norm +
-      0.05 * destroy_score +
-      0.05 * attrition_score +
-      0.05 * pcv_score +
-      0.05 * threat_score
-    )
 
   # --- Context adjustments ---
+  score = np.where(direct_damage >= opponent.life, np.maximum(score, 0.95), score)
+  score = np.where(unblocked_in  >= player.life,   np.minimum(score, 0.05), score)
 
-  # Lethal takes priority regardless of personality
-  if direct_damage >= opponent.life:
-    score = max(score, 0.95)
-
-  if unblocked_incoming >= player.life:
-    score = min(score, 0.05)
-
-  # Against god-card decks: cover all slots so their big cards can't attack freely
   if opponent.deck_type in ("God Card", "Pantheon"):
-    score = min(1.0, score + 0.08 * cover_score)
+    score = np.minimum(1.0, score + 0.08 * cover_score)
-
-  # Against aggro/rush: need to block more urgently
   if opponent.deck_type in ("Aggro", "Rush"):
-    score = min(1.0, score + 0.06 * block_score + 0.04 * threat_score)
+    score = np.minimum(1.0, score + 0.06 * block_score + 0.04 * threat_score)
-
-  # Against wall decks: direct damage matters more than destroying cards
   if opponent.deck_type == "Wall":
-    score = min(1.0, score + 0.06 * atk_score)
+    score = np.minimum(1.0, score + 0.06 * atk_score)
-
-  # Press the advantage when opponent is low on life
   if opponent.life < STARTING_LIFE * 0.3:
-    score = min(1.0, score + 0.06 * atk_score)
+    score = np.minimum(1.0, score + 0.06 * atk_score)
-
-  # Prioritize survival when low on life
   if player.life < STARTING_LIFE * 0.3:
-    score = min(1.0, score + 0.06 * threat_score + 0.04 * block_score)
+    score = np.minimum(1.0, score + 0.06 * threat_score + 0.04 * block_score)
+  if opp_cards_left <= 5:
+    score = np.where(cards_on_board > 0, np.minimum(1.0, score + 0.05), score)
 
-  # Opponent running low on cards: keep a card on board for attrition win condition
+  return np.maximum(0.0, score - sac_penalty)
-  if opponent_cards_left <= 5 and cards_on_board > 0:
-    score = min(1.0, score + 0.05)
-
-  # Apply sacrifice penalty last so it can override all other considerations.
-  score = max(0.0, score - sacrifice_penalty)
-
-  return score
 
 
-# ==================== Turn execution ====================
+async def choose_plan(player: PlayerState, opponent: PlayerState, personality: AIPersonality, difficulty: int) -> MovePlan:
+  plans = generate_plans(player, opponent)
+
+  scores = score_plans_batch(plans, player, opponent, personality)
+
+  noise_scale = (max(0,11 - difficulty)**2) * 0.01 - 0.01
+  noise = np.random.normal(0, noise_scale, len(scores)).astype(np.float32)
+  return plans[int(np.argmax(scores + noise))]
 
 async def run_ai_turn(game_id: str):
   from game_manager import (
@@ -485,24 +388,10 @@ async def run_ai_turn(game_id: str):
         pass
 
   # --- Generate and score candidate plans ---
-  plans = generate_plans(player, opponent)
+  best_plan = await choose_plan(player, opponent, personality, difficulty)
-
-  if difficulty <= 2:
-    # Actively bad
-    scored = [(score_plan(p, player, opponent, personality) + random.gauss(0, 0.15*difficulty), p)
-              for p in plans]
-    best_plan = min(scored, key=lambda x: x[0])[1]
-  elif difficulty == 3:
-    # Fully random
-    best_plan = random.choice(plans)
-  else:
-    noise = max(0.0, ((8 - difficulty) / 6.0) * 0.30)
-    scored = [(score_plan(p, player, opponent, personality) + random.gauss(0, noise), p)
-              for p in plans]
-    best_plan = max(scored, key=lambda x: x[0])[1]
 
   logger.info(
-    f"AI turn: d={difficulty} p={personality.value} plan={best_plan.label} plans={len(plans)} " +
+    f"AI turn: d={difficulty} p={personality.value} plan={best_plan.label} " +
     f"sac={best_plan.sacrifice_slots} plays={[c.name for c, _ in best_plan.plays]}"
   )
 

backend/card.py

@@ -541,7 +541,17 @@ async def _get_specific_card_async(title: str) -> Card|None:
 
 # Sync entrypoints
 def generate_cards(size: int) -> list[Card]:
-  return asyncio.run(_get_cards_async(size))
+  cards = []
+  remaining = size
+  while remaining > 0:
+    batch = min(remaining,10)
+    logger.warning(f"Generating {batch} cards ({len(cards)}/{size})")
+    cards += asyncio.run(_get_cards_async(batch))
+    remaining = size - len(cards)
+    if remaining > 0:
+      sleep(4)
+
+  return cards
 
 def generate_card(title: str) -> Card|None:
   return asyncio.run(_get_specific_card_async(title))

backend/game.py

@@ -6,7 +6,7 @@ from datetime import datetime
 
 from models import Card as CardModel
 
-STARTING_LIFE = 500
+STARTING_LIFE = 1000
 MAX_ENERGY_CAP = 6
 BOARD_SIZE = 5
 HAND_SIZE = 5

backend/main.py

@@ -538,11 +538,7 @@ if __name__ == "__main__":
   from card import generate_cards, Card
   from time import sleep
 
-  all_cards: list[Card] = []
+  all_cards = generate_cards(500)
-  for i in range(30):
-    print(i)
-    all_cards += generate_cards(10)
-    sleep(5)
 
   all_cards.sort(key=lambda x: x.cost, reverse=True)
 

backend/simulate.py

@@ -0,0 +1,440 @@
+import json
+import os
+import random
+import uuid
+import asyncio
+from concurrent.futures import ProcessPoolExecutor
+from dotenv import load_dotenv
+load_dotenv()
+
+from datetime import datetime
+
+from card import Card, CardType, CardRarity, generate_cards, compute_deck_type
+from game import (
+  CardInstance, PlayerState, GameState,
+  action_play_card, action_sacrifice, action_end_turn,
+)
+from ai import AIPersonality, choose_cards, choose_plan
+
+SIMULATION_CARDS_PATH = os.path.join(os.path.dirname(__file__), "simulation_cards.json")
+SIMULATION_CARD_COUNT = 1000
+
+
+# ==================== Card pool ====================
+
+def _card_to_dict(card: Card) -> dict:
+  return {
+    "name": card.name,
+    "created_at": card.created_at.isoformat(),
+    "image_link": card.image_link,
+    "card_rarity": card.card_rarity.name,
+    "card_type": card.card_type.name,
+    "wikidata_instance": card.wikidata_instance,
+    "text": card.text,
+    "attack": card.attack,
+    "defense": card.defense,
+    "cost": card.cost,
+  }
+
+
+def _dict_to_card(d: dict) -> Card:
+  return Card(
+    name=d["name"],
+    created_at=datetime.fromisoformat(d["created_at"]),
+    image_link=d["image_link"],
+    card_rarity=CardRarity[d["card_rarity"]],
+    card_type=CardType[d["card_type"]],
+    wikidata_instance=d["wikidata_instance"],
+    text=d["text"],
+    attack=d["attack"],
+    defense=d["defense"],
+    cost=d["cost"],
+  )
+
+
+def get_simulation_cards() -> list[Card]:
+  if os.path.exists(SIMULATION_CARDS_PATH):
+    with open(SIMULATION_CARDS_PATH, "r", encoding="utf-8") as f:
+      data = json.load(f)
+    return [_dict_to_card(d) for d in data]
+
+  print(f"Generating {SIMULATION_CARD_COUNT} cards (this may take a while)...")
+  cards = generate_cards(SIMULATION_CARD_COUNT)
+
+  with open(SIMULATION_CARDS_PATH, "w", encoding="utf-8") as f:
+    json.dump([_card_to_dict(c) for c in cards], f, ensure_ascii=False, indent=2)
+
+  print(f"Saved {len(cards)} cards to {SIMULATION_CARDS_PATH}")
+  return cards
+
+
+# ==================== Single game ====================
+
+PLAYER1_ID = "p1"
+PLAYER2_ID = "p2"
+MAX_TURNS = 300  # safety cap to prevent infinite games
+
+
+def _make_instances(deck: list[Card]) -> list[CardInstance]:
+  return [
+    CardInstance(
+      instance_id=str(uuid.uuid4()),
+      card_id=card.name,
+      name=card.name,
+      attack=card.attack,
+      defense=card.defense,
+      max_defense=card.defense,
+      cost=card.cost,
+      card_type=card.card_type.name,
+      card_rarity=card.card_rarity.name,
+      image_link=card.image_link or "",
+      text=card.text or "",
+    )
+    for card in deck
+  ]
+
+
+async def simulate_game(
+  cards: list[Card],
+  difficulty1: int,
+  personality1: AIPersonality,
+  difficulty2: int,
+  personality2: AIPersonality,
+) -> str | None:
+  """
+  Simulate a single game between two AIs choosing from `cards`.
+  Player 1 always goes first.
+
+  Returns "p1", "p2", or None if the game exceeds MAX_TURNS.
+
+  Designed to be awaited inside asyncio.gather() to run many games concurrently.
+  """
+  deck1 = choose_cards(cards, difficulty1, personality1)
+  deck2 = choose_cards(cards, difficulty2, personality2)
+
+  instances1 = _make_instances(deck1)
+  instances2 = _make_instances(deck2)
+  random.shuffle(instances1)
+  random.shuffle(instances2)
+
+  deck_type1 = compute_deck_type(deck1) or "Balanced"
+  deck_type2 = compute_deck_type(deck2) or "Balanced"
+
+  p1 = PlayerState(user_id=PLAYER1_ID, username="AI1", deck_type=deck_type1, deck=instances1)
+  p2 = PlayerState(user_id=PLAYER2_ID, username="AI2", deck_type=deck_type2, deck=instances2)
+
+  # P1 always goes first
+  p1.increment_energy_cap()
+  p2.increment_energy_cap()
+  p1.refill_energy()
+  p1.draw_to_full()
+
+  state = GameState(
+    game_id=str(uuid.uuid4()),
+    players={PLAYER1_ID: p1, PLAYER2_ID: p2},
+    player_order=[PLAYER1_ID, PLAYER2_ID],
+    active_player_id=PLAYER1_ID,
+    phase="main",
+    turn=1,
+  )
+
+  configs = {
+    PLAYER1_ID: (difficulty1, personality1),
+    PLAYER2_ID: (difficulty2, personality2),
+  }
+
+  for _ in range(MAX_TURNS):
+    if state.result:
+      break
+
+    active_id = state.active_player_id
+    difficulty, personality = configs[active_id]
+    player = state.players[active_id]
+    opponent = state.players[state.opponent_id(active_id)]
+
+    plan = await choose_plan(player, opponent, personality, difficulty)
+
+    for slot in plan.sacrifice_slots:
+      if player.board[slot] is not None:
+        action_sacrifice(state, slot)
+
+    plays = list(plan.plays)
+    random.shuffle(plays)
+    for card, slot in plays:
+      hand_idx = next((i for i, c in enumerate(player.hand) if c is card), None)
+      if hand_idx is None:
+        continue
+      if player.board[slot] is not None:
+        continue
+      if card.cost > player.energy:
+        continue
+      action_play_card(state, hand_idx, slot)
+
+    action_end_turn(state)
+
+  if state.result and state.result.winner_id:
+    return state.result.winner_id
+  return None
+
+
+# ==================== Process-pool worker ====================
+# These must be module-level so they are picklable.
+
+_worker_cards: list[Card] = []
+
+def _init_worker(cards: list[Card]) -> None:
+  global _worker_cards
+  _worker_cards = cards
+
+def _run_game_sync(args: tuple) -> str | None:
+  """Synchronous entry point for a worker process."""
+  d1, p1_name, d2, p2_name = args
+  return asyncio.run(simulate_game(
+    _worker_cards,
+    d1, AIPersonality(p1_name),
+    d2, AIPersonality(p2_name),
+  ))
+
+
+# ==================== Tournament ====================
+
+def _all_players(difficulties: list[int] | None = None) -> list[tuple[AIPersonality, int]]:
+  """Return all (personality, difficulty) combinations for the given difficulties (default 1-10)."""
+  if difficulties is None:
+    difficulties = list(range(1, 11))
+  return [
+    (personality, difficulty)
+    for personality in AIPersonality
+    for difficulty in difficulties
+  ]
+
+
+def _player_label(personality: AIPersonality, difficulty: int) -> str:
+  return f"{personality.value[:3].upper()}-{difficulty}"
+
+
+async def run_tournament(
+  cards: list[Card],
+  games_per_matchup: int = 5,
+  difficulties: list[int] | None = None,
+) -> dict[tuple[int, int], int]:
+  """
+  Pit every (personality, difficulty) pair against every other, as both
+  first and second player.
+
+  `difficulties` selects which difficulty levels to include (default: 1-10).
+
+  Returns a wins dict keyed by (first_player_index, second_player_index)
+  where the value is how many of `games_per_matchup` games the first player won.
+
+  Games run in parallel across all CPU cores via ProcessPoolExecutor.
+  Cards are sent to each worker once at startup, not once per game.
+  """
+  players = _all_players(difficulties)
+  n = len(players)
+
+  # Build the flat list of (i, j, args) for every game
+  indexed_args: list[tuple[int, int, tuple]] = []
+  for i in range(n):
+    p1_personality, p1_difficulty = players[i]
+    for j in range(n):
+      p2_personality, p2_difficulty = players[j]
+      args = (p1_difficulty, p1_personality.value, p2_difficulty, p2_personality.value)
+      for _ in range(games_per_matchup):
+        indexed_args.append((i, j, args))
+
+  total_games = len(indexed_args)
+  n_workers = os.cpu_count() or 1
+  print(f"Running {total_games} games across {n_workers} workers "
+        f"({n} players, {games_per_matchup} games per ordered pair)...")
+
+  done = [0]
+  report_every = max(1, total_games // 200)
+
+  loop = asyncio.get_running_loop()
+
+  async def tracked(future):
+    result = await future
+    done[0] += 1
+    if done[0] % report_every == 0 or done[0] == total_games:
+      pct = done[0] / total_games * 100
+      print(f"  {done[0]}/{total_games} games done ({pct:.1f}%)", end="\r", flush=True)
+    return result
+
+  with ProcessPoolExecutor(
+    max_workers=n_workers,
+    initializer=_init_worker,
+    initargs=(cards,),
+  ) as executor:
+    futures = [
+      loop.run_in_executor(executor, _run_game_sync, args)
+      for _, _, args in indexed_args
+    ]
+    results = await asyncio.gather(*[tracked(f) for f in futures])
+
+  print("\nFinished")
+
+  wins: dict[tuple[int, int], int] = {}
+  ties = 0
+  for (i, j, _), winner in zip(indexed_args, results):
+    key = (i, j)
+    if key not in wins:
+      wins[key] = 0
+    if winner == PLAYER1_ID:
+      wins[key] += 1
+    elif winner is None:
+      ties += 1
+
+  print(f"Ties: {ties}")
+
+  return wins
+
+
+def rank_players(
+  wins: dict[tuple[int, int], int],
+  games_per_matchup: int,
+  players: list[tuple[AIPersonality, int]],
+) -> list[int]:
+  """
+  Rank player indices by total wins (as first + second player combined).
+  Returns indices sorted worst-to-best.
+  """
+  n = len(players)
+  total_wins = [0] * n
+
+  for (i, j), p1_wins in wins.items():
+    if i == j:
+      continue  # self-matchups are symmetric; skip to avoid double-counting
+    p2_wins = games_per_matchup - p1_wins
+    total_wins[i] += p1_wins
+    total_wins[j] += p2_wins
+
+  return sorted(range(n), key=lambda k: total_wins[k])
+
+
+TOURNAMENT_RESULTS_PATH = os.path.join(os.path.dirname(__file__), "tournament_results.json")
+
+
+def save_tournament(
+  wins: dict[tuple[int, int], int],
+  games_per_matchup: int,
+  players: list[tuple[AIPersonality, int]],
+  path: str = TOURNAMENT_RESULTS_PATH,
+):
+  data = {
+    "games_per_matchup": games_per_matchup,
+    "players": [
+      {"personality": p.value, "difficulty": d}
+      for p, d in players
+    ],
+    "wins": {f"{i},{j}": w for (i, j), w in wins.items()},
+  }
+  with open(path, "w", encoding="utf-8") as f:
+    json.dump(data, f, indent=2)
+  print(f"Tournament results saved to {path}")
+
+
+def load_tournament(path: str = TOURNAMENT_RESULTS_PATH) -> tuple[dict[tuple[int, int], int], int, list[tuple[AIPersonality, int]]]:
+  """Returns (wins, games_per_matchup, players)."""
+  with open(path, "r", encoding="utf-8") as f:
+    data = json.load(f)
+  wins = {
+    (int(k.split(",")[0]), int(k.split(",")[1])): v
+    for k, v in data["wins"].items()
+  }
+  players = [
+    (AIPersonality(p["personality"]), p["difficulty"])
+    for p in data["players"]
+  ]
+  return wins, data["games_per_matchup"], players
+
+
+def draw_grid(
+  wins: dict[tuple[int, int], int],
+  games_per_matchup: int = 5,
+  players: list[tuple[AIPersonality, int]] | None = None,
+  output_path: str = "tournament_grid.png",
+):
+  """
+  Draw a heatmap grid of tournament results.
+
+  Rows  = first player
+  Cols  = second player
+  Color = red if first  player won more of their games in that cell
+          green if second player won more
+  ×     = one player swept all games in that cell
+  """
+  import matplotlib
+  matplotlib.use("Agg")
+  import matplotlib.pyplot as plt
+  import matplotlib.colors as mcolors
+  import numpy as np
+
+  if players is None:
+    players = _all_players()
+  n = len(players)
+  ranked = rank_players(wins, games_per_matchup, players)  # worst-to-best indices
+
+  labels = [_player_label(*players[i]) for i in ranked]
+
+  # Build value matrix: (p1_wins - p2_wins) / games_per_matchup ∈ [-1, 1], NaN on diagonal
+  matrix = np.full((n, n), np.nan)
+  for row, i in enumerate(ranked):
+    for col, j in enumerate(ranked):
+      p1_wins = wins.get((i, j), 0)
+      matrix[row, col] = (p1_wins - (games_per_matchup - p1_wins)) / games_per_matchup
+
+  cell_size = 0.22
+  fig_size = n * cell_size + 3
+  fig, ax = plt.subplots(figsize=(fig_size, fig_size))
+
+  cmap = mcolors.LinearSegmentedColormap.from_list(
+    "p1_p2", ["#90EE90", "#67A2E0", "#D74E4E"]  # pastel green → blue → red
+  )
+  norm = mcolors.Normalize(vmin=-1, vmax=1)
+
+  img = ax.imshow(matrix, cmap=cmap, norm=norm, aspect="equal", interpolation="none")
+
+  # × marks for sweeps
+  for row, i in enumerate(ranked):
+    for col, j in enumerate(ranked):
+      p1_wins = wins.get((i, j), 0)
+      if p1_wins == games_per_matchup or p1_wins == 0:
+        ax.text(col, row, "×", ha="center", va="center",
+                fontsize=5, color="black", fontweight="bold", zorder=3)
+
+  ax.set_xticks(range(n))
+  ax.set_yticks(range(n))
+  ax.set_xticklabels(labels, rotation=90, fontsize=4)
+  ax.set_yticklabels(labels, fontsize=4)
+  ax.xaxis.set_label_position("top")
+  ax.xaxis.tick_top()
+
+  ax.set_xlabel("Second player", labelpad=8, fontsize=8)
+  ax.set_ylabel("First player", labelpad=8, fontsize=8)
+  ax.set_title(
+    "Tournament results — red: first player wins more,  green: second player wins more",
+    pad=14, fontsize=9,
+  )
+
+  plt.colorbar(img, ax=ax, fraction=0.015, pad=0.01,
+               label="(P1 wins - P2 wins) / games per cell")
+
+  plt.tight_layout()
+  plt.savefig(output_path, dpi=150, bbox_inches="tight")
+  plt.close()
+  print(f"Grid saved to {output_path}")
+
+
+if __name__ == "__main__":
+  import sys
+
+  GAMES_PER_MATCHUP = 50
+
+  difficulties = list(range(1, 11))
+
+  card_pool = get_simulation_cards()
+  players = _all_players(difficulties)
+  wins = asyncio.run(run_tournament(card_pool, games_per_matchup=GAMES_PER_MATCHUP, difficulties=difficulties))
+  save_tournament(wins, games_per_matchup=GAMES_PER_MATCHUP, players=players)
+  draw_grid(wins, games_per_matchup=GAMES_PER_MATCHUP, players=players)