[fix] Non heuristic moves...

rl/model.py

@@ -0,0 +1,54 @@
+"""Compact dual-head neural network (policy + value) sized for ESP32."""
+
+from .config import CONV_FILTERS, NUM_CONV_LAYERS, DENSE_UNITS, LEARNING_RATE
+
+
+def build_model():
+    """Build a small AlphaZero-style network.
+
+    Input:  (6, 7, 2)  — current player pieces / opponent pieces
+    Output: policy (7,) — log-probabilities over columns
+            value  (1,) — board evaluation in [-1, 1]
+    """
+    from tensorflow import keras
+    from tensorflow.keras import layers
+
+    inp = layers.Input(shape=(6, 7, 2), name="board")
+
+    x = inp
+    for i in range(NUM_CONV_LAYERS):
+        x = layers.Conv2D(
+            CONV_FILTERS, 3, padding="same", activation="relu", name=f"conv{i}"
+        )(x)
+        x = layers.BatchNormalization(name=f"bn{i}")(x)
+
+    flat = layers.Flatten(name="flat")(x)
+    shared = layers.Dense(DENSE_UNITS, activation="relu", name="shared_dense")(flat)
+
+    # Policy head
+    policy = layers.Dense(7, name="policy_logits")(shared)
+
+    # Value head
+    value = layers.Dense(1, activation="tanh", name="value")(shared)
+
+    model = keras.Model(inputs=inp, outputs=[policy, value], name="connect4_net")
+
+    model.compile(
+        optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),
+        loss={
+            "policy_logits": keras.losses.CategoricalCrossentropy(from_logits=True),
+            "value": keras.losses.MeanSquaredError(),
+        },
+        loss_weights={"policy_logits": 1.0, "value": 1.0},
+    )
+    return model
+
+
+def print_model_info(model):
+    model.summary()
+    total_params = model.count_params()
+    approx_size_kb = total_params * 4 / 1024  # float32
+    approx_int8_kb = total_params / 1024       # int8
+    print(f"\nTotal parameters: {total_params:,}")
+    print(f"Approx size (float32): {approx_size_kb:.1f} KB")
+    print(f"Approx size (int8):    {approx_int8_kb:.1f} KB")