add tile permutations to ai knowledge

blokus.py

@@ -98,24 +98,42 @@ def encode_move(tile_idx: int, placement: tuple[int, int]) -> torch.Tensor:
     return torch.cat([tile_vec, pos_vec], dim=0)  # 23-dim
 
 
-def encode_state_and_move(game_state, player: int, tile_idx: int, placement):
+def encode_state_and_move(
+    game_state, player: int, tile_idx: int, placement: tuple[int, int], perm: game.Tile
+):
     board, p1tiles, p2tiles = game_state
-    board_tensor = encode_board(board).flatten()  # 3*14*14 = 588
-    tiles_tensor = encode_tiles(p1tiles, p2tiles)  # 42
-    move_tensor = encode_move(tile_idx, placement)  # 23
 
-    # Encode "current player" as a bit
+    # Encode board BEFORE the move
+    board_before = encode_board(board).flatten()
+
+    # Encode board AFTER the move using sim_place
+    gp = game.Player.P1 if player == 1 else game.Player.P2
+    board_after_sim = board.sim_place(
+        perm, placement, gp
+    )  # <--- uses your new function
+    board_after = encode_board(board_after_sim).flatten()
+
+    tiles_tensor = encode_tiles(p1tiles, p2tiles)
+    move_tensor = encode_move(tile_idx, placement)  # still tile+position encoding
     player_tensor = torch.tensor([1.0 if player == 1 else 0.0], dtype=torch.float32)
 
-    return torch.cat([board_tensor, tiles_tensor, move_tensor, player_tensor], dim=0)
-    # Total size = 588 + 42 + 23 + 1 = 654
+    return torch.cat(
+        [
+            board_before,  # 588
+            board_after,  # 588
+            tiles_tensor,  # 42
+            move_tensor,  # 23
+            player_tensor,  # 1
+        ],
+        dim=0,
+    )
 
 
 ###########
 # Model   #
 ###########
 
-FEATURE_SIZE = 654  # from above
+FEATURE_SIZE = 1242  # from above
 
 
 class MoveScorer(nn.Module):
@@ -222,7 +240,7 @@ class MLAgent(Agent):
         # Build feature batch
         features = []
         for tidx, perm, placement in moves:
-            feat = encode_state_and_move(game_state, player, tidx, placement)
+            feat = encode_state_and_move(game_state, player, tidx, placement, perm)
             features.append(feat)
         X = torch.stack(features, dim=0)
 
@@ -256,7 +274,7 @@ def select_move_and_logprob(model: MoveScorer, game_state, player: int):
 
     features = []
     for tidx, perm, placement in moves:
-        feat = encode_state_and_move(game_state, player, tidx, placement)
+        feat = encode_state_and_move(game_state, player, tidx, placement, perm)
         features.append(feat)
     X = torch.stack(features, dim=0)  # (num_moves, FEATURE_SIZE)