add Training for AI and AI Model and allow to collect rl data from BestBattleSnake

server/TrainBattleSnakeAI.py

@@ -0,0 +1,290 @@
+import argparse, random, glob, json, math
+from collections import Counter
+from pathlib import Path
+
+MOVES = ["up", "down", "left", "right"]
+
+def resolve_input_files(inputs:list[str]) -> list[Path]:
+  resolved:list[Path] = []
+  seen:set[str] = set()
+
+  for item in inputs:
+    path = Path(item)
+    if path.is_dir():
+      for file_path in sorted(path.rglob("*.jsonl")):
+        key = str(file_path.resolve())
+        if key in seen:
+          continue
+        seen.add(key)
+        resolved.append(file_path)
+      continue
+
+    if any(ch in item for ch in "*?[]"):
+      for match in sorted(glob.glob(item)):
+        file_path = Path(match)
+        if not file_path.is_file():
+          continue
+        key = str(file_path.resolve())
+        if key in seen:
+          continue
+        seen.add(key)
+        resolved.append(file_path)
+      continue
+
+    if path.is_file():
+      key = str(path.resolve())
+      if key in seen:
+        continue
+      seen.add(key)
+      resolved.append(path)
+
+  return resolved
+
+def _neighbors(x:int, y:int) -> list[tuple[int, int, str]]:
+  return [
+    (x, y + 1, "up"),
+    (x, y - 1, "down"),
+    (x - 1, y, "left"),
+    (x + 1, y, "right"),
+  ]
+
+def _safe_neighbor_count(point:tuple[int, int], blocked:set[tuple[int, int]], width:int, height:int) -> int:
+  count = 0
+  for nx, ny, _ in _neighbors(point[0], point[1]):
+    if not (0 <= nx < width and 0 <= ny < height):
+      continue
+    if (nx, ny) in blocked:
+      continue
+    count += 1
+  return count
+
+def _manhattan_to_nearest_food(point: tuple[int, int], food: set[tuple[int, int]]) -> int:
+  if not food:
+    return 25
+  return min(abs(point[0] - fx) + abs(point[1] - fy) for fx, fy in food)
+
+def extract_feature_values(row:dict) -> dict[str, float]:
+  board = row.get("game_board", {})
+  snakes = board.get("snakes", [])
+  if not snakes:
+    return {}
+
+  me = snakes[0]
+  body = me.get("body", [])
+  if not body:
+    return {}
+
+  width = int(board.get("width", 0))
+  height = int(board.get("height", 0))
+  head = body[0]
+  hx = int(head.get("x", 0))
+  hy = int(head.get("y", 0))
+  health = int(me.get("health", 100))
+  length = int(me.get("length", len(body)))
+
+  food_set = {(int(f.get("x", 0)), int(f.get("y", 0))) for f in board.get("food", [])}
+  hazard_set = {(int(h.get("x", 0)), int(h.get("y", 0))) for h in board.get("hazards", [])}
+  blocked = set()
+  for snake in snakes:
+    for seg in snake.get("body", []):
+      blocked.add((int(seg.get("x", 0)), int(seg.get("y", 0))))
+
+  features:dict[str, float] = {
+    "bias": 1.0,
+    "health_norm": max(0.0, min(1.0, health / 100.0)),
+    "length_norm": min(1.0, length / max(1.0, width * height)),
+    "turn_norm": min(1.0, int(row.get("turn", 0)) / 100.0),
+    "food_count_norm": min(1.0, len(food_set) / 10.0),
+    "hazard_count_norm": min(1.0, len(hazard_set) / 20.0),
+    "opponent_count_norm": min(1.0, max(0, len(snakes) - 1) / 7.0),
+  }
+
+  safe_total = 0
+  for nx, ny, move in _neighbors(hx, hy):
+    in_bounds = 1.0 if (0 <= nx < width and 0 <= ny < height) else 0.0
+    blocked_next = 1.0 if (nx, ny) in blocked else 0.0
+    food_next = 1.0 if (nx, ny) in food_set else 0.0
+    hazard_next = 1.0 if (nx, ny) in hazard_set else 0.0
+
+    if in_bounds and not blocked_next:
+      safe_total += 1
+      open_next = float(_safe_neighbor_count((nx, ny), blocked, width, height))
+      dist_food = float(_manhattan_to_nearest_food((nx, ny), food_set))
+    else:
+      open_next = 0.0
+      dist_food = 25.0
+
+    prefix = f"m:{move}:"
+    features[prefix + "in_bounds"] = in_bounds
+    features[prefix + "blocked"] = blocked_next
+    features[prefix + "food"] = food_next
+    features[prefix + "hazard"] = hazard_next
+    features[prefix + "open_next"] = min(4.0, open_next) / 4.0
+    features[prefix + "food_dist"] = min(25.0, dist_food) / 25.0
+
+  features["safe_total_norm"] = safe_total / 4.0
+  return features
+
+class SoftmaxMoveModel:
+  def __init__(self):
+    self.weights = {move: {} for move in MOVES}
+    self.bias = {move: 0.0 for move in MOVES}
+
+  def _score(self, move:str, features:dict[str, float]) -> float:
+    weight_map = self.weights[move]
+    value = self.bias[move]
+    for name, feat in features.items():
+      value += weight_map.get(name, 0.0) * feat
+    return value
+
+  def fit(self, rows:list[dict], epochs:int=14, lr:float=0.08, l2:float=1e-6) -> None:
+    examples = []
+    for row in rows:
+      label = row.get("move")
+      if label not in MOVES:
+        continue
+      features = extract_feature_values(row)
+      if not features:
+        continue
+      examples.append((features, label))
+
+    if not examples:
+      return
+
+    for _ in range(epochs):
+      random.shuffle(examples)
+      for features, label in examples:
+        scores = {move: self._score(move, features) for move in MOVES}
+        max_score = max(scores.values())
+        exp_scores = {
+          move: math.exp(scores[move] - max_score) for move in MOVES
+        }
+        z = sum(exp_scores.values())
+        probs = {move: exp_scores[move] / z for move in MOVES}
+
+        for move in MOVES:
+          target = 1.0 if move == label else 0.0
+          gradient = target - probs[move]
+          self.bias[move] += lr * gradient
+
+          w = self.weights[move]
+          for name, feat in features.items():
+            current = w.get(name, 0.0)
+            update = lr * ((gradient * feat) - (l2 * current))
+            w[name] = current + update
+
+  def predict_scores(self, row:dict) -> dict[str, float]:
+    features = extract_feature_values(row)
+    if not features:
+      return {move: 0.0 for move in MOVES}
+    return {move: self._score(move, features) for move in MOVES}
+
+  def predict(self, row:dict) -> str:
+    scores = self.predict_scores(row)
+    return max(scores, key=lambda move: scores[move])
+
+  def evaluate(self, rows:list[dict]) -> dict:
+    total = 0
+    correct = 0
+    top2 = 0
+    confusion = {move: Counter() for move in MOVES}
+
+    for row in rows:
+      expected = row.get("move")
+      if expected not in MOVES:
+        continue
+
+      scores = self.predict_scores(row)
+      ranked = sorted(scores.items(), key=lambda item: item[1], reverse=True)
+      predicted = ranked[0][0]
+
+      total += 1
+      if predicted == expected:
+        correct += 1
+      if expected in {
+        ranked[0][0],
+        ranked[1][0] if len(ranked) > 1 else ranked[0][0],
+      }:
+        top2 += 1
+
+      confusion[expected][predicted] += 1
+
+    return {
+      "total": total,
+      "correct": correct,
+      "accuracy": round((correct / total) if total else 0.0, 4),
+      "top2_accuracy": round((top2 / total) if total else 0.0, 4),
+      "confusion": {label: dict(confusion[label]) for label in MOVES},
+    }
+
+  def to_dict(self) -> dict:
+    return {
+      "model_type": "softmax_moves_v2",
+      "moves": MOVES,
+      "weights": self.weights,
+      "bias": self.bias,
+    }
+
+def read_rows(paths:list[Path]) -> list[dict]:
+  rows: list[dict] = []
+  for path in paths:
+    with path.open("r", encoding="utf-8") as handle:
+      for line in handle:
+        if not line.strip():
+          continue
+        row = json.loads(line)
+        if row.get("move") not in MOVES:
+          continue
+        if not row.get("game_board"):
+          continue
+        rows.append(row)
+  return rows
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser(description="Train Battlesnake move model")
+  parser.add_argument("--input", action="append", required=True)
+  parser.add_argument("--output", required=True)
+  parser.add_argument("--eval-split", type=float, default=0.2)
+  parser.add_argument("--seed", type=int, default=42)
+  parser.add_argument("--epochs", type=int, default=14)
+  parser.add_argument("--lr", type=float, default=0.08)
+  args = parser.parse_args()
+
+  paths = resolve_input_files(args.input)
+  if not paths:
+    raise SystemExit("No input files found")
+
+  rows = read_rows(paths)
+  if len(rows) < 50:
+    raise SystemExit("Need at least 50 rows for training")
+
+  random.seed(args.seed)
+  random.shuffle(rows)
+  eval_count = int(len(rows) * max(0.0, min(0.5, args.eval_split)))
+  eval_rows = rows[:eval_count]
+  train_rows = rows[eval_count:]
+
+  model = SoftmaxMoveModel()
+  model.fit(train_rows, epochs=max(1, args.epochs), lr=max(1e-4, args.lr))
+  metrics = model.evaluate(eval_rows)
+
+  output = Path(args.output)
+  output.parent.mkdir(parents=True, exist_ok=True)
+  payload = {
+    "input_files": [str(p) for p in paths],
+    "train_rows": len(train_rows),
+    "eval_rows": len(eval_rows),
+    "eval_metrics": metrics,
+    "model": model.to_dict(),
+  }
+  output.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+
+  print(json.dumps({
+      "output": str(output),
+      "train_rows": len(train_rows),
+      "eval_rows": len(eval_rows),
+      "accuracy": metrics.get("accuracy"),
+      "top2_accuracy": metrics.get("top2_accuracy"),
+    },
+    indent=2,
+  ))