Update Environment_Wrapper.py

Environment_Wrapper.py

@@ -1,72 +1,74 @@
-import gymnasium as gym
-import numpy as np
-from collections import deque
-import cv2 
-
-class CarRacingEnvWrapper(gym.Wrapper):
-    def __init__(self, env, num_stack_frames=4, grayscale=True, resize_dim=(84, 84)):
-        super().__init__(env)
-        self.num_stack_frames = num_stack_frames
-        self.grayscale = grayscale
-        self.resize_dim = resize_dim 
-
-        self.frames = deque(maxlen=num_stack_frames)
-
-        original_shape = self.env.observation_space.shape
-        if grayscale:
-        
-            original_shape = original_shape[:-1] 
-
-        if resize_dim:
-            self.observation_shape = (resize_dim[1], resize_dim[0])
-        else:
-            self.observation_shape = original_shape[:2]
-
-        self.observation_space = gym.spaces.Box(
-            low=0, high=255,
-            shape=(self.observation_shape[0], self.observation_shape[1], num_stack_frames),
-            dtype=np.uint8 
-        )
-
-        self.OFF_TRACK_PENALTY_SCALE = 0.1 
-        self.GRASS_COLOR_THRESHOLD = 180 
-
-    def _preprocess_frame(self, frame):
-
-        if self.grayscale:
-            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-        if self.resize_dim:
-            frame = cv2.resize(frame, self.resize_dim, interpolation=cv2.INTER_AREA)
-        return frame
-
-    def reset(self, **kwargs):
-        observation, info = self.env.reset(**kwargs)
-        processed_frame = self._preprocess_frame(observation)
-
-        for _ in range(self.num_stack_frames):
-            self.frames.append(processed_frame)
-
-        stacked_frames = np.stack(self.frames, axis=-1)
-        return stacked_frames, info
-
-    def step(self, action):
-        
-        observation, reward, terminated, truncated, info = self.env.step(action)
-
-        modified_reward = reward
-
-        is_on_grass = np.mean(observation[:, :, 1]) > self.GRASS_COLOR_THRESHOLD
-        
-        if is_on_grass:
-            modified_reward -= self.OFF_TRACK_PENALTY_SCALE
-           
-        info['is_on_grass'] = is_on_grass
-        info['original_reward'] = reward
-        info['modified_reward'] = modified_reward
-
-        processed_frame = self._preprocess_frame(observation)
-
-        self.frames.append(processed_frame)
-        stacked_frames = np.stack(self.frames, axis=-1)
-
+#G
+#D
+import gymnasium as gym
+import numpy as np
+from collections import deque
+import cv2 
+
+class CarRacingEnvWrapper(gym.Wrapper):
+    def __init__(self, env, num_stack_frames=4, grayscale=True, resize_dim=(84, 84)):
+        super().__init__(env)
+        self.num_stack_frames = num_stack_frames
+        self.grayscale = grayscale
+        self.resize_dim = resize_dim 
+
+        self.frames = deque(maxlen=num_stack_frames)
+
+        original_shape = self.env.observation_space.shape
+        if grayscale:
+        
+            original_shape = original_shape[:-1] 
+
+        if resize_dim:
+            self.observation_shape = (resize_dim[1], resize_dim[0])
+        else:
+            self.observation_shape = original_shape[:2]
+
+        self.observation_space = gym.spaces.Box(
+            low=0, high=255,
+            shape=(self.observation_shape[0], self.observation_shape[1], num_stack_frames),
+            dtype=np.uint8 
+        )
+
+        self.OFF_TRACK_PENALTY_SCALE = 0.1 
+        self.GRASS_COLOR_THRESHOLD = 180 
+
+    def _preprocess_frame(self, frame):
+
+        if self.grayscale:
+            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+        if self.resize_dim:
+            frame = cv2.resize(frame, self.resize_dim, interpolation=cv2.INTER_AREA)
+        return frame
+
+    def reset(self, **kwargs):
+        observation, info = self.env.reset(**kwargs)
+        processed_frame = self._preprocess_frame(observation)
+
+        for _ in range(self.num_stack_frames):
+            self.frames.append(processed_frame)
+
+        stacked_frames = np.stack(self.frames, axis=-1)
+        return stacked_frames, info
+
+    def step(self, action):
+        
+        observation, reward, terminated, truncated, info = self.env.step(action)
+
+        modified_reward = reward
+
+        is_on_grass = np.mean(observation[:, :, 1]) > self.GRASS_COLOR_THRESHOLD
+        
+        if is_on_grass:
+            modified_reward -= self.OFF_TRACK_PENALTY_SCALE
+           
+        info['is_on_grass'] = is_on_grass
+        info['original_reward'] = reward
+        info['modified_reward'] = modified_reward
+
+        processed_frame = self._preprocess_frame(observation)
+
+        self.frames.append(processed_frame)
+        stacked_frames = np.stack(self.frames, axis=-1)
+
         return stacked_frames, modified_reward, terminated, truncated, info