diff --git a/settings.py b/settings.py
index 8d1ffce..02bd246 100644
--- a/settings.py
+++ b/settings.py
@@ -1,48 +1,80 @@
+"""
+File containing the settings.
+"""
+# based on: https://github.com/Sentdex/Carla-RL/blob/master/settings.py
+
 # Carla environment settings
-CARLA_PATH = '../CARLA_0.9.6_Python_3.7'  # Path to Carla root folder
-CARLA_HOSTS_TYPE = 'local'  # 'local' or 'remote', 'local' means that script can start and restart Carla Simulator
+# CARLA_PATH = './carla_0.9.6'  # Path to Carla root folder
+CARLA_PATH = "./carla"  # Path to Carla root folder
+CARLA_HOSTS_TYPE = "remote"  # 'local' or 'remote', 'local' means that script can start and restart Carla Simulator
 CARLA_HOSTS_NO = 1
-CARLA_HOSTS = [['localhost', 2000, 10], ['localhost', 2002, 10]]  # List of hosts and ports and worlds to use, at least 2 ports of difference as Carla uses N and N+1 port, Town01 to Town97 for world currently, Town01 to Town07 for world are currently available, int number instead - random world change interval in minutes
+CARLA_HOSTS = [
+    ["localhost", 2000, 10],
+    ["localhost", 2002, 10],
+]  # List of hosts and ports and worlds to use, at least 2 ports of difference as Carla uses N and N+1 port, Town01 to Town97 for world currently, Town01 to Town07 for world are currently available, int number instead - random world change interval in minutes
 SECONDS_PER_EPISODE = 10
 EPISODE_FPS = 60  # Desired
 IMG_WIDTH = 480
 IMG_HEIGHT = 270
 CAR_NPCS = 50
-RESET_CAR_NPC_EVERY_N_TICKS = 1  # Resets one car NPC every given number of ticks, tick is about a second
-ACTIONS = ['forward', 'forward_left', 'forward_right', 'brake', 'brake_left', 'brake_right']  # ['forward', 'left', 'right', 'forward_left', 'forward_right', 'backwards', 'backwards_left', 'backwards_right']
+RESET_CAR_NPC_EVERY_N_TICKS = (
+    1  # Resets one car NPC every given number of ticks, tick is about a second
+)
+ACTIONS = [
+    "forward",
+    "forward_left",
+    "forward_right",
+    "brake",
+    "brake_left",
+    "brake_right",
+]  # ['forward', 'left', 'right', 'forward_left', 'forward_right', 'backwards', 'backwards_left', 'backwards_right']
 WEIGHT_REWARDS_WITH_EPISODE_PROGRESS = False  # Linearly weights rewards from 0 to 1 with episode progress (from 0 up to SECONDS_PER_EPISODE)
-WEIGHT_REWARDS_WITH_SPEED = 'linear'  # 'discrete': -1 < 50kmh, 1 otherwise, 'linear': -1..1 with 0..100kmh, 'quadratic': -1..1 with 0..100kmh with formula: (speed / 100) ** 1.3 * 2 - 1
+WEIGHT_REWARDS_WITH_SPEED = "linear"  # 'discrete': -1 < 50kmh, 1 otherwise, 'linear': -1..1 with 0..100kmh, 'quadratic': -1..1 with 0..100kmh with formula: (speed / 100) ** 1.3 * 2 - 1
 SPEED_MIN_REWARD = -1
 SPEED_MAX_REWARD = 1
-PREVIEW_CAMERA_RES = [[640, 400, -5, 0, 2.5], [1280, 800, -5, 0, 2.5]]  # Available resolutions from "above the car" preview camera [width, height, x, y, z], where x, y and z are related to car position
-COLLISION_FILTER = [['static.sidewalk', -1], ['static.road', -1], ['vehicle.', 500]]  # list of pairs: agent id (can be part of the name) and impulse value allowed (-1 - disable collision detection entirely)
+PREVIEW_CAMERA_RES = [
+    [640, 400, -5, 0, 2.5],
+    [1280, 800, -5, 0, 2.5],
+]  # Available resolutions from "above the car" preview camera [width, height, x, y, z], where x, y and z are related to car position
+COLLISION_FILTER = [
+    ["static.sidewalk", -1],
+    ["static.road", -1],
+    ["vehicle.", 500],
+]  # list of pairs: agent id (can be part of the name) and impulse value allowed (-1 - disable collision detection entirely)

 # Agent settings
 AGENTS = 1
 AGENT_MEMORY_FRACTION = 0.1
-AGENT_GPU = None  # None, a number (to use given GPU for all agents) or a list - example [0, 1, 1] (first agent - GPU 0, 2nd and 3rd GPU 1)
-AGENT_CARLA_INSTANCE = []  # Empty list for first Carla instance or list in size of AGENTS with Carla instance bounds for agents, for excample [1, 1, 2, 2]
+AGENT_GPU = 0  # None, a number (to use given GPU for all agents) or a list - example [0, 1, 1] (first agent - GPU 0, 2nd and 3rd GPU 1)
+AGENT_CARLA_INSTANCE = (
+    []
+)  # Empty list for first Carla instance or list in size of AGENTS with Carla instance bounds for agents, for excample [1, 1, 2, 2]
 UPDATE_WEIGHTS_EVERY = 0  # How frequently to update weights (compared to trainer fits), 0 for episode start only
 AGENT_SHOW_PREVIEW = []  # List of agent id's so show a preview, or empty list
 AGENT_SYNCED = True  # Synchronizes agent with frame updates from Carla
-AGENT_IMG_TYPE = 'grayscaled'  # 'rgb', 'grayscaled' or 'stacked' (stacks last 3 consecutive grayscaled frames)
-AGENT_ADDITIONAL_DATA = ['kmh']  # What additional data to include next to image data in observation space, possible values: kmh
+AGENT_IMG_TYPE = "grayscaled"  # 'rgb', 'grayscaled' or 'stacked' (stacks last 3 consecutive grayscaled frames)
+AGENT_ADDITIONAL_DATA = [
+    "kmh"
+]  # What additional data to include next to image data in observation space, possible values: kmh

 # Trainer settings
 MINIBATCH_SIZE = 16  # How many steps (samples) to use for training
 PREDICTION_BATCH_SIZE = 1  # How many samples to predict at once (the more, the faster)
-TRAINING_BATCH_SIZE = MINIBATCH_SIZE // 2  # How many samples to fit at once (the more, the faster) - should be MINIBATCH_SIZE divided by power of 2
+TRAINING_BATCH_SIZE = (
+    MINIBATCH_SIZE // 2
+)  # How many samples to fit at once (the more, the faster) - should be MINIBATCH_SIZE divided by power of 2
 UPDATE_TARGET_EVERY = 100  # Terminal states (end of episodes)
-MODEL_NAME = '5_residual_#CNN_KERNELS#'  # model name, prefixed from sources/models.py, #MODEL_ARCHITECTURE# adds model architectore acronym, #CNN_KERNELS# adds number of kernels from all CNN layers
+MODEL_NAME = "5_residual_#CNN_KERNELS#"  # model name, prefixed from sources/models.py, #MODEL_ARCHITECTURE# adds model architectore acronym, #CNN_KERNELS# adds number of kernels from all CNN layers
 MIN_REWARD = 100  # For model save
 TRAINER_MEMORY_FRACTION = 0.6
-TRAINER_GPU = None  # None - not set, 0, 1, ... - GPU with given index
+TRAINER_GPU = 1  # None - not set, 0, 1, ... - GPU with given index
 SAVE_CHECKPOINT_EVERY = 100  # episodes

 # DQN settings
 DISCOUNT = 0.99
 REPLAY_MEMORY_SIZE = 20_000  # How many last steps to keep for model training
-MIN_REPLAY_MEMORY_SIZE = 5_000  # Minimum number of steps in a memory to start training
+# ~ MIN_REPLAY_MEMORY_SIZE = 5_000  # Minimum number of steps in a memory to start training
+MIN_REPLAY_MEMORY_SIZE = 100  # Minimum number of steps in a memory to start training

 # Exploration settings
 START_EPSILON = 1
@@ -50,16 +82,16 @@ EPSILON_DECAY = 0.99995  # 0.99975
 MIN_EPSILON = 0.1

 # Model settings
-MODEL_BASE = '5_residual_CNN'  # from models.py
-MODEL_HEAD = 'hidden_dense'  # from models.py
-MODEL_SETTINGS = {'hidden_1_units': 256}  # 'hidden_1_units': 1024 for Xception
+MODEL_BASE = "5_residual_CNN"  # from models.py
+MODEL_HEAD = "hidden_dense"  # from models.py
+MODEL_SETTINGS = {"hidden_1_units": 256}  # 'hidden_1_units': 1024 for Xception

 # Optimizer settings
 OPTIMIZER_LEARNING_RATE = 0.001
 OPTIMIZER_DECAY = 0.0

 # Conv Cam
-CONV_CAM_LAYER = 'auto_act'  # 'auto' - finds and uses last activation layer, 'auto_act' - uses Activation layer after last convolution layer if exists
+CONV_CAM_LAYER = "auto_act"  # 'auto' - finds and uses last activation layer, 'auto_act' - uses Activation layer after last convolution layer if exists
 CONV_CAM_AGENTS = [1]

 # Console settings
diff --git a/sources/console.py b/sources/console.py
index 6e74566..6efbc63 100644
--- a/sources/console.py
+++ b/sources/console.py
@@ -186,7 +186,8 @@ class ConsoleStats:
             lines.append('')

             # Sets cursor back at the beginning of text block (moves it up)
-            string = '\r' + ('\033[A' * len(lines))
+            # ~ string = '\r' + ('\033[A' * len(lines))
+            string = ""

             # Add spaces to form a 70-char long line
             for line in lines:
@@ -197,7 +198,8 @@ class ConsoleStats:
             if self.stop.value == STOP.stopped:
                 return

-            time.sleep(0.2)
+            # ~ time.sleep(0.2)
+            time.sleep(3)

     @staticmethod
     def print_short(fps_counter, env, qs, action, action_name):
diff --git a/sources/trainer.py b/sources/trainer.py
index 366b4f9..479d9cc 100644
--- a/sources/trainer.py
+++ b/sources/trainer.py
@@ -96,6 +96,7 @@ class ARTDQNTrainer(ARTDQNAgent):

         # Start training only if certain number of transitions is already being saved in replay memory
         if len(self.replay_memory) < settings.MIN_REPLAY_MEMORY_SIZE:
+            print(f"too small replay_memory: {len(self.replay_memory)} vs {settings.MIN_REPLAY_MEMORY_SIZE}")
             return False

         # Get a minibatch of random samples from memory replay table
@@ -221,6 +222,7 @@ class ARTDQNTrainer(ARTDQNAgent):
             # If .train() call returns false, there's not enough transitions in replay memory
             # Just wait (and exit on 'stop' signal)
             if not self.train():
+                print("train returned false")
                 self.trainer_stats[0] = TRAINER_STATE.waiting

                 # Trainer is also a manager for stopping everything as it has to save a checkpoint
@@ -241,6 +243,7 @@ class ARTDQNTrainer(ARTDQNAgent):
             # Training part finished here, measure time and convert it to number of trains per second
             frame_time = time.time() - step_start
             self.tps_counter.append(frame_time)
+            print("training finished")
             self.trainer_stats[1] = len(self.tps_counter)/sum(self.tps_counter)

             # Shared flag set by models when they performed good to save a model
@@ -367,6 +370,8 @@ def run(model_path, logdir, stop, weights, weights_iteration, episode, epsilon,

     configured_actions = [getattr(ACTIONS, action) for action in settings.ACTIONS]

+    print("trainer helo")
+
     # Iterate over episodes until 'stop' signal
     while stop.value != 3:

@@ -375,15 +380,18 @@ def run(model_path, logdir, stop, weights, weights_iteration, episode, epsilon,
             trainer.tensorboard.step = episode.value

         # Load new transitions put here by models and place then im memory replay table
+        # ~ print(f"num transitions: {transitions.qsize()}")
         for _ in range(transitions.qsize()):
             try:
                 trainer.update_replay_memory(transitions.get(True, 0.1))
             except:
                 break

+
         # Log stats in tensorboard
         while not tensorboard_stats.empty():

+            # ~ print("trainer-loop2")
             # Added to a Queue by agents
             agent_episode, reward, agent_epsilon, episode_time, frame_time, weighted_reward, *avg_predicted_qs = tensorboard_stats.get_nowait()