Update rules: free draw/pass, remove Q in 2-player games

- Players can freely choose to draw even with playable cards
- After drawing, players may pass instead of playing
- Remove Q cards from deck in 2-player games (reverse has no effect)
- Use greedy random opponent in evaluation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2 files changed, 16 insertions, 3 deletions

diff --git a/blazing_env.py b/blazing_env.py
index c3d97ae..c440293 100644
--- a/blazing_env.py
+++ b/blazing_env.py
@@ -15,7 +15,10 @@ Special cards:
 
 Rules:
   - Match top card by suit OR rank (unless playing 8 or Swap)
-  - Can't play → draw 1; if drawn card is playable, play it immediately
+  - Player may freely choose to draw even if they have playable cards
+  - After drawing, player may play any playable card OR pass (end turn)
+  - Each turn allows at most one draw
+  - If no playable cards and deck is empty, player must pass
   - First player to empty hand wins
   - Initial hand: 5 cards each
 """
@@ -92,7 +95,9 @@ class BlazingEightsEnv:
             self.rng = np.random.default_rng(seed)
 
         # Build & shuffle deck
-        deck = list(range(NUM_CARDS))
+        # In 2-player games, remove Q cards (reverse has no effect)
+        deck = [c for c in range(NUM_CARDS)
+                if not (self.num_players == 2 and card_rank(c) == RANK_Q)]
         self.rng.shuffle(deck)
 
         # Deal 5 cards each
diff --git a/train.py b/train.py
index e955c09..7f85267 100644
--- a/train.py
+++ b/train.py
@@ -100,6 +100,14 @@ class Transition:
         self.legal_mask = legal_mask
 
 
+def greedy_random_action(legal: list[int]) -> int:
+    """Pick a random playable card; only draw/pass if no card to play."""
+    play_actions = [a for a in legal if a < NUM_CARDS or (56 <= a <= 59)]
+    if play_actions:
+        return int(np.random.choice(play_actions))
+    return int(np.random.choice(legal))
+
+
 def collect_game(env: BlazingEightsEnv, model: PolicyValueNet, device="cpu"):
     """
     Play one full game, return per-player trajectories.
@@ -389,7 +397,7 @@ def evaluate_vs_random(model: PolicyValueNet, num_players=2, num_games=1000, dev
             if player == 0:
                 action, _, _ = model.get_action(obs, legal, device)
             else:
-                action = np.random.choice(legal)
+                action = greedy_random_action(legal)
             obs, rewards, done, info = env.step(action)
             if done:
                 if env.winner == 0: