use 4 by 6 instead of 3 by N

model.py

@@ -175,29 +175,3 @@ def outputs_to_action(output):
     action = actions.ALL[position_index]
 
     return action
-
-
-def action_to_outputs(action):
-    """
-    Converts an action into a target tensor.
-
-    This function takes an action (LEFT, RIGHT, or other) and converts it into a target tensor with three elements.
-    The tensor's elements correspond to the actions LEFT, forward, and RIGHT respectively. The element corresponding
-    to the given action is set to 1, and the others are set to 0.
-
-    Args:
-        action (str): The action to convert. Should be one of the actions defined in the `actions` class.
-
-    Returns:
-        torch.Tensor: A tensor of shape (3,) where the element corresponding to the given action is 1, and the others are 0.
-    """
-    target = torch.zeros(7)
-
-    try:
-        action_index = actions.ALL.index(action)
-    except ValueError:
-        action_index = 0
-
-    target[action_index] = 1
-
-    return target

mydriver.py

@@ -50,36 +50,28 @@
 
 def build_lane_view(world):
     """
-    Build a 3xN 2D array representation of the world based on the car's lane and x position.
+    Build a 6x4 2D array representation of the world based on the car's lane and x position.
 
     Args:
         world (World): An instance of the World class providing read-only
                        access to the current game state.
 
     Returns:
-        list[list[str]]: 3xN array representation of the world view from the car, where N is the specified height.
+        list[list[str]]: 6x4 array representation of the world view from the car, where 4 is the specified height.
                           The bottom line is one line above the car's y position, and the top line is the line height lines above that.
                           The array provides a view of the world from the car's perspective, with the car's y position excluded.
 
     Notes:
         The function uses the car's y position to determine the vertical range of the 2D array.
-        The starting x-coordinate is determined by the car's lane. If the lane is 0, the starting x is 0. If the lane is 1, the starting x is 3.
-        The function also provides a wrapper around world.get to handle negative y values, returning an empty string for such cases.
     """
     height = 4
+    width = 6
     car_y = world.car.y
 
-    # Calculate the starting y-coordinate based on the car's y position and the desired height
-    start_y = car_y - height
-
-    # Wrapper around world.get to handle negative y values
-    def get_value(j, i):
-        if i < 0:
-            return ""
-        return world.get((j, i))
-
     # Generate the 2D array from start_y up to world.car.y
-    array = [[get_value(j, i) for j in range(6)] for i in range(start_y, car_y)]
+    array = [
+        [world.get((j, i)) for j in range(width)] for i in range(car_y - height, car_y)
+    ]
 
     return array
 

train.py

@@ -29,7 +29,7 @@
 import torch.nn as nn
 import torch.optim as optim
 
-from model import DriverModel, action_to_outputs, actions, obstacles, view_to_inputs
+from model import DriverModel, actions, obstacles, view_to_inputs
 
 # Training parameters
 num_epochs = 0
@@ -105,6 +105,32 @@ def driver_simulator(array, car_x):
     return action
 
 
+def action_to_outputs(action):
+    """
+    Converts an action into a target tensor.
+
+    This function takes an action (LEFT, RIGHT, or other) and converts it into a target tensor with three elements.
+    The tensor's elements correspond to the actions LEFT, forward, and RIGHT respectively. The element corresponding
+    to the given action is set to 1, and the others are set to 0.
+
+    Args:
+        action (str): The action to convert. Should be one of the actions defined in the `actions` class.
+
+    Returns:
+        torch.Tensor: A tensor of shape (3,) where the element corresponding to the given action is 1, and the others are 0.
+    """
+    target = torch.zeros(7)
+
+    try:
+        action_index = actions.ALL.index(action)
+    except ValueError:
+        action_index = 0
+
+    target[action_index] = 1
+
+    return target
+
+
 def generate_batch(batch_size):
     """
     Generates a batch of samples for training.