Merge pull request #4 from zucchero-sintattico/deepsource-autofix-bd3…

…f32ff

refactor: remove unnecessary whitespace

model-gym-ros-env/car_node/car_node/main.py

@@ -27,10 +27,10 @@
 SPEED_SCALE = 0.7
 
 class PPOModelEvaluator(Node):
-    
+
     def __init__(self):
         super().__init__('wall_follow_node')
-        
+
         #Topics & Subs, Pubs
         lidarscan_topic = '/scan'
         drive_topic = '/drive'
@@ -49,12 +49,12 @@ def __init__(self):
     def lidar_callback(self, data):
         d = np.array(data.ranges, dtype=np.float64)
         d = convert_range(d, [data.angle_min, data.angle_max], [-1, 1])
-        
+
         action, _states = self.model.predict(d, deterministic=True)
         action = self.eval_env.un_normalise_actions(action)
-        
+
         self.get_logger().info(f'{action[0], action[1]}')
-        
+
         drive_msg = AckermannDriveStamped()
         drive_msg.header.stamp = self.get_clock().now().to_msg()
         drive_msg.header.frame_id = "laser"

model-gym-ros-env/car_node/car_node/wrapper.py

@@ -71,7 +71,7 @@ def __init__(self, env, random_map=False):
 
     def get_total_steps(self) -> int:
         return self.step_count
-    
+
     def get_episode_rewards(self) -> List[float]:
         """
         Returns the rewards of all the episodes
@@ -81,7 +81,7 @@ def get_episode_rewards(self) -> List[float]:
         return self.episode_returns
 
 
-  
+
 
 
     def set_raceliens(self):
@@ -128,13 +128,13 @@ def step(self, action):
                                         ('c3B/stream', self.race_line_color))
 
         reward = 0
-
-
 
-
+
+
+
         if self.count < len(self.race_line_x) - 1:
             X, Y = observation['poses_x'][0], observation['poses_y'][0]
-        
+
             dist = np.sqrt(np.power((X - next_x), 2) + np.power((Y - next_y), 2))
             if dist < 2:
                 self.count = self.count + 1
@@ -156,7 +156,7 @@ def step(self, action):
             + 
             np.power((observation["poses_y"][0] - self.last_position['y']), 2)
                                             )
-        
+
         # if distance_from_last_position > 0.0005:
         #     reward += 0.8
         # else:
@@ -194,7 +194,7 @@ def step(self, action):
 
         self.episode_returns.append(reward)
 
-        
+
 
 
         return self.normalise_observations(observation['scans'][0]), reward, bool(done), info
@@ -222,7 +222,7 @@ def reset(self):
         race_line_x = race_line_x[random_index:] + race_line_x[:random_index]
         race_line_y = race_line_y[random_index:] + race_line_y[:random_index]
         race_line_theta = race_line_theta[random_index:] + race_line_theta[:random_index]
-        
+
 
         self.race_line_x = race_line_x
         self.race_line_y = race_line_y
@@ -261,4 +261,4 @@ def un_normalise_actions(self, actions):
     def normalise_observations(self, observations):
         # convert observations from normal lidar distances range to range [-1, 1]
         return convert_range(observations, [self.lidar_min, self.lidar_max], [-1, 1])
-    
+

src/train.py

@@ -32,7 +32,7 @@ def train(
     eval_env.set_map_path(path)
     eval_env.seed(1773449316)
     eval_env.set_optimize_speed(optimize_speed)
-    
+
 
     timesteps_list = np.logspace(np.log10(min_timesteps), np.log10(max_timesteps), num=num_of_steps, endpoint=True, base=10.0, dtype=int, axis=0)
     learning_rate_list = np.logspace(np.log10(max_learning_rate), np.log10(min_learning_rate), num=num_of_steps, endpoint=True, base=10.0, dtype=None, axis=0)
@@ -43,7 +43,7 @@ def train(
     for timesteps, learning_rate in zip(timesteps_list, learning_rate_list):
 
         eval_env.seed(round(np.random.rand()*1000000000))
-        
+
         if os.path.exists("./train_test/best_global_model.zip"):
             print("Loading Existing Model")
             model = PPO.load("./train_test/best_global_model", eval_env, learning_rate=linear_schedule(learning_rate), device=device)

src/wrapper/wrapper.py

@@ -14,7 +14,7 @@ def logger(map, event, reword, lap_time):
     with open('log.csv', 'a', newline='') as file:
         writer = csv.writer(file)
         writer.writerow([map, event, reword, lap_time])
-        
+
 
 def convert_range(value, input_range, output_range):
     # converts value(s) from range to another range
@@ -62,7 +62,7 @@ def __init__(self, env, random_map=False):
 
         # set threshold for maximum angle of car, to prevent spinning
         self.max_theta = 100
-        
+
 
         self.map_path = None
         self.random_map = random_map
@@ -121,11 +121,11 @@ def start_position(self):
 
 
     def step(self, action):
-        
+
         def episode_end(reason = None, rew = 0):
             if reason is not None:
                 print("Episode End ->", reason, self.map_path)
-                
+
             done = True
             self.count = 0
             self.episode_returns = []
@@ -148,8 +148,8 @@ def episode_end(reason = None, rew = 0):
                                         ('c3B/stream', self.race_line_color))
 
         reward = 0
-        
-        
+
+
         aceleration_reward = action_convert[1]
 
         if self.optimize_speed:
@@ -159,11 +159,11 @@ def episode_end(reason = None, rew = 0):
                 reward += 2
 
         reward = reward * 0.01
-    
-        
+
+
         if self.count < len(self.race_line_x) - 1:
             X, Y = observation['poses_x'][0], observation['poses_y'][0]
-        
+
             dist = np.sqrt(np.power((X - next_x), 2) + np.power((Y - next_y), 2))
             if dist < 2:
                 self.count = self.count + 1
@@ -172,54 +172,54 @@ def episode_end(reason = None, rew = 0):
 
             if dist < 2.5:
                 self.number_of_base_reward_give += 1
-                
+
                 if self.number_of_base_reward_give < 100:
                     reward += 0.05 
                 else:
                     reward -= 1
-                        
+
             if dist > 3: 
                 reward -= 1
-                
+
 
         else:  
             steps_goal = self.count       
             if  not self.one_lap_done:
                 steps_done = self.step_for_episode          
             elif self.one_lap_done:
                 steps_done = self.step_for_episode / 2   
-                
+
             k = (steps_done - steps_goal)/steps_goal
-            
+
             reward += (1-k) * 100 
 
             print("----------------- Lap Done ----------------->", self.map_path, self.step_for_episode * 0.01, reward)
-            
+
             self.count = 0
-            
+
             if self.one_lap_done:
                 logger(self.map_path, "lap_done", sum(self.episode_returns), self.step_for_episode * 0.01)
                 self.episode_returns = []
                 self.step_for_episode = 0
                 self.one_lap_done = False
             else:
                 self.one_lap_done = True
-            
-        
+
+
         reward = round(reward, 6)
-        
+
 
 
         if observation['collisions'][0]:
             logger(self.map_path, "collisions", sum(self.episode_returns), self.step_for_episode * 0.01)
             done, reward = episode_end(rew = -30)
-            
-            
+
+
 
         if self.step_for_episode > 50_000:
             logger(self.map_path, "too_slow", sum(self.episode_returns), self.step_for_episode * 0.01)
             done, reward = episode_end("Too long", -10)
-            
+
         self.episode_returns.append(reward)
         self.step_for_episode += 1
 
@@ -250,7 +250,7 @@ def reset(self):
         race_line_x = race_line_x[random_index:] + race_line_x[:random_index]
         race_line_y = race_line_y[random_index:] + race_line_y[:random_index]
         race_line_theta = race_line_theta[random_index:] + race_line_theta[:random_index]
-        
+
 
         self.race_line_x = race_line_x
         self.race_line_y = race_line_y
@@ -290,4 +290,4 @@ def un_normalise_actions(self, actions):
     def normalise_observations(self, observations):
         # convert observations from normal lidar distances range to range [-1, 1]
         return convert_range(observations, [self.lidar_min, self.lidar_max], [-1, 1])
-    
+