Introduce BehavioralModel and TestBrain classes for behavior modeling

world/base/brain.py

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+from world.behavorial import BehavioralModel
+
+
+class TestBrain(BehavioralModel):
+    def __init__(self):
+        super().__init__()
+        # Define input keys
+        self.inputs = {
+            'distance': 0.0,  # Distance from a food object
+            'angle': 0.0  # Relative angle to a food object
+        }
+
+        # Define output keys
+        self.outputs = {
+            'acceleration': 0.0,  # Linear acceleration
+            'angular_acceleration': 0.0  # Angular acceleration
+        }
+
+        self.weights = {
+            'distance': 1.0,
+            'angle': 1.0
+        }
+
+    def tick(self, input_data) -> dict:
+        """
+        Process inputs and produce corresponding outputs.
+
+        :param input_data: Dictionary containing 'distance' and 'angle' values
+        :return: Dictionary with 'acceleration' and 'angular_acceleration' values
+        """
+        # Update internal input state
+        self.inputs['distance'] = input_data.get('distance', 0.0)
+        self.inputs['angle'] = input_data.get('angle', 0.0)
+
+        # Initialize output dictionary
+        output_data = {
+            'acceleration': 0.0,
+            'angular_acceleration': 0.0
+        }
+
+        return output_data

world/behavorial.py

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+class BehavioralModel:
+    """
+    BehaviorModel is a superclass that defines the interface for all behavior models.
+
+    It has a variable but runtime-fixed number of inputs and outputs
+    """
+
+    def __init__(self, ):
+        self.inputs = {}
+        self.outputs = {}
+
+    def tick(self, input_data) -> dict:
+        """
+        Processes the given input data and produces a corresponding output dictionary.
+
+        This function serves as a placeholder or basic structure for processing input data
+        and preparing the output. The specific functionality should be implemented according
+        to the requirements of the application.
+
+        :param input_data: Input data to be processed.
+        :type input_data: Dict
+        :return: A dictionary containing the processed output data.
+        :rtype: Dict
+        """
+        output_data = {}
+
+        return output_data