diff --git a/apps/donkeycars/default_car/myconfig.py b/apps/donkeycars/default_car/myconfig.py
index 89b43e2..35f4a9d 100644
--- a/apps/donkeycars/default_car/myconfig.py
+++ b/apps/donkeycars/default_car/myconfig.py
@@ -1,16 +1,20 @@
 # """
 # My CAR CONFIG
+
 # This file is read by your car application's manage.py script to change the car
 # performance
+
 # If desired, all config overrides can be specified here.
 # The update operation will not touch this file.
 # """
-# import os
+
+import os
+
 #
 # #PATHS
-# CAR_PATH = PACKAGE_PATH = os.path.dirname(os.path.realpath(__file__))
-# DATA_PATH = os.path.join(CAR_PATH, 'data')
-# MODELS_PATH = os.path.join(CAR_PATH, 'models')
+CAR_PATH = PACKAGE_PATH = os.path.dirname(os.path.realpath(__file__))
+DATA_PATH = os.path.join(CAR_PATH, "data")
+MODELS_PATH = os.path.join(CAR_PATH, "models")
 #
 # #VEHICLE
 # DRIVE_LOOP_HZ = 20      # the vehicle loop will pause if faster than this speed.
@@ -18,12 +22,12 @@
 #
 # #CAMERA
 # CAMERA_TYPE = "PICAM"   # (PICAM|WEBCAM|CVCAM|CSIC|V4L|D435|MOCK|IMAGE_LIST)
-# IMAGE_W = 160
-# IMAGE_H = 128
-# IMAGE_DEPTH = 3         # default RGB=3, make 1 for mono
+IMAGE_W = 160
+IMAGE_H = 120
+IMAGE_DEPTH = 3  # default RGB=3, make 1 for mono
 # CAMERA_FRAMERATE = DRIVE_LOOP_HZ
-CAMERA_VFLIP = True
-CAMERA_HFLIP = True
+# CAMERA_VFLIP = False
+# CAMERA_HFLIP = False
 # # For CSIC camera - If the camera is mounted in a rotated position, changing the below parameter will correct the output frame orientation
 # CSIC_CAM_GSTREAMER_FLIP_PARM = 0 # (0 => none , 4 => Flip horizontally, 6 => Flip vertically)
 #
@@ -47,9 +51,9 @@
 # DRIVE_TRAIN_TYPE = "SERVO_ESC" # SERVO_ESC|DC_STEER_THROTTLE|DC_TWO_WHEEL|DC_TWO_WHEEL_L298N|SERVO_HBRIDGE_PWM|PIGPIO_PWM|MM1|MOCK
 #
 # #STEERING
-STEERING_CHANNEL = 0  # channel on the 9685 pwm board 0-15
-STEERING_LEFT_PWM = 525  # pwm value for full left steering
-STEERING_RIGHT_PWM = 225  # pwm value for full right steering
+# STEERING_CHANNEL = 1            #channel on the 9685 pwm board 0-15
+# STEERING_LEFT_PWM = 460         #pwm value for full left steering
+# STEERING_RIGHT_PWM = 290        #pwm value for full right steering
 #
 # #STEERING FOR PIGPIO_PWM
 # STEERING_PWM_PIN = 13           #Pin numbering according to Broadcom numbers
@@ -57,10 +61,10 @@
 # STEERING_PWM_INVERTED = False   #If PWM needs to be inverted
 #
 # #THROTTLE
-THROTTLE_CHANNEL = 1  # channel on the 9685 pwm board 0-15
-THROTTLE_FORWARD_PWM = 400  # pwm value for max forward throttle
-THROTTLE_STOPPED_PWM = 370  # pwm value for no movement
-THROTTLE_REVERSE_PWM = 325  # pwm value for max reverse throttle
+# THROTTLE_CHANNEL = 0            #channel on the 9685 pwm board 0-15
+# THROTTLE_FORWARD_PWM = 500      #pwm value for max forward throttle
+# THROTTLE_STOPPED_PWM = 370      #pwm value for no movement
+# THROTTLE_REVERSE_PWM = 220      #pwm value for max reverse throttle
 #
 # #THROTTLE FOR PIGPIO_PWM
 # THROTTLE_PWM_PIN = 18           #Pin numbering according to Broadcom numbers
@@ -122,16 +126,16 @@
 # #line parameter --type to the python manage.py train and drive commands.
 # # tensorflow models: (linear|categorical|tflite_linear|tensorrt_linear)
 # # pytorch models: (resnet18)
-DEFAULT_MODEL_TYPE = "categorical"
+# DEFAULT_MODEL_TYPE = 'linear'
 # BATCH_SIZE = 128                #how many records to use when doing one pass of gradient decent. Use a smaller number if your gpu is running out of memory.
 # TRAIN_TEST_SPLIT = 0.8          #what percent of records to use for training. the remaining used for validation.
-# MAX_EPOCHS = 100                #how many times to visit all records of your data
+MAX_EPOCHS = 3  # how many times to visit all records of your data
 # SHOW_PLOT = True                #would you like to see a pop up display of final loss?
 # VERBOSE_TRAIN = True            #would you like to see a progress bar with text during training?
 # USE_EARLY_STOP = True           #would you like to stop the training if we see it's not improving fit?
 # EARLY_STOP_PATIENCE = 5         #how many epochs to wait before no improvement
 # MIN_DELTA = .0005               #early stop will want this much loss change before calling it improved.
-# PRINT_MODEL_SUMMARY = True      #print layers and weights to stdout
+PRINT_MODEL_SUMMARY = True  # print layers and weights to stdout
 # OPTIMIZER = None                #adam, sgd, rmsprop, etc.. None accepts default
 # LEARNING_RATE = 0.001           #only used when OPTIMIZER specified
 # LEARNING_RATE_DECAY = 0.0       #only used when OPTIMIZER specified
@@ -159,11 +163,11 @@
 #
 # #WEB CONTROL
 # WEB_CONTROL_PORT = int(os.getenv("WEB_CONTROL_PORT", 8887))  # which port to listen on when making a web controller
-# WEB_INIT_MODE = "local"              # which control mode to start in. one of user|local_angle|local. Setting local will start in ai mode.
+WEB_INIT_MODE = "local"
 #
 # #JOYSTICK
 # USE_JOYSTICK_AS_DEFAULT = False      #when starting the manage.py, when True, will not require a --js option to use the joystick
-JOYSTICK_MAX_THROTTLE = 1.0  # this scalar is multiplied with the -1 to 1 throttle value to limit the maximum throttle. This can help if you drop the controller or just don't need the full speed available.
+# JOYSTICK_MAX_THROTTLE = 0.5         #this scalar is multiplied with the -1 to 1 throttle value to limit the maximum throttle. This can help if you drop the controller or just don't need the full speed available.
 # JOYSTICK_STEERING_SCALE = 1.0       #some people want a steering that is less sensitve. This scalar is multiplied with the steering -1 to 1. It can be negative to reverse dir.
 # AUTO_RECORD_ON_THROTTLE = True      #if true, we will record whenever throttle is not zero. if false, you must manually toggle recording with some other trigger. Usually circle button on joystick.
 # CONTROLLER_TYPE = 'xbox'            #(ps3|ps4|xbox|nimbus|wiiu|F710|rc3|MM1|custom) custom will run the my_joystick.py controller written by the `donkey createjs` command
@@ -230,9 +234,7 @@
 #
 # #RECORD OPTIONS
 # RECORD_DURING_AI = False        #normally we do not record during ai mode. Set this to true to get image and steering records for your Ai. Be careful not to use them to train.
-AUTO_CREATE_NEW_TUB = (
-    True  # create a new tub (tub_YY_MM_DD) directory when recording or append records to data directory directly
-)
+# AUTO_CREATE_NEW_TUB = False     #create a new tub (tub_YY_MM_DD) directory when recording or append records to data directory directly
 #
 # #LED
 # HAVE_RGB_LED = False            #do you have an RGB LED like https://www.amazon.com/dp/B07BNRZWNF
@@ -291,15 +293,17 @@
 # #This enables that, and sets the path to the simualator and the environment.
 # #You will want to download the simulator binary from: https://github.com/tawnkramer/donkey_gym/releases/download/v18.9/DonkeySimLinux.zip
 # #then extract that and modify DONKEY_SIM_PATH.
-# DONKEY_GYM = False
-# DONKEY_SIM_PATH = "path to sim" #"/home/tkramer/projects/sdsandbox/sdsim/build/DonkeySimLinux/donkey_sim.x86_64" when racing on virtual-race-league use "remote", or user "remote" when you want to start the sim manually first.
-# DONKEY_GYM_ENV_NAME = "donkey-generated-track-v0" # ("donkey-generated-track-v0"|"donkey-generated-roads-v0"|"donkey-warehouse-v0"|"donkey-avc-sparkfun-v0")
-# GYM_CONF = { "body_style" : "donkey", "body_rgb" : (128, 128, 128), "car_name" : "car", "font_size" : 100} # body style(donkey|bare|car01) body rgb 0-255
+DONKEY_GYM = True
+DONKEY_SIM_PATH = "remote"  # "/home/tkramer/projects/sdsandbox/sdsim/build/DonkeySimLinux/donkey_sim.x86_64" when racing on virtual-race-league use "remote", or user "remote" when you want to start the sim manually first.
+DONKEY_GYM_ENV_NAME = "donkey-generated-track-v0"  # ("donkey-generated-track-v0"|"donkey-generated-roads-v0"|"donkey-warehouse-v0"|"donkey-avc-sparkfun-v0")
+GYM_CONF = {"body_style": "donkey", "font_size": 20}  # body style(donkey|bare|car01) body rgb 0-255
+GYM_CONF["car_name"] = "v-twin"
+GYM_CONF["body_rgb"] = (255, 0, 0)
 # GYM_CONF["racer_name"] = "Your Name"
 # GYM_CONF["country"] = "Place"
 # GYM_CONF["bio"] = "I race robots."
 #
-# SIM_HOST = "127.0.0.1"              # when racing on virtual-race-league use host "trainmydonkey.com"
+SIM_HOST = "ec2-54-158-84-85.compute-1.amazonaws.com"
 # SIM_ARTIFICIAL_LATENCY = 0          # this is the millisecond latency in controls. Can use useful in emulating the delay when useing a remote server. values of 100 to 400 probably reasonable.
 #
 # # Save info from Simulator (pln)
@@ -318,7 +322,7 @@
 # AI_LAUNCH_KEEP_ENABLED = False      # when False ( default) you will need to hit the AI_LAUNCH_ENABLE_BUTTON for each use. This is safest. When this True, is active on each trip into "local" ai mode.
 #
 # #Scale the output of the throttle of the ai pilot for all model types.
-AI_THROTTLE_MULT = 1.65  # this multiplier will scale every throttle value for all output from NN models
+AI_THROTTLE_MULT = 1.5  # this multiplier will scale every throttle value for all output from NN models
 #
 # #Path following
 # PATH_FILENAME = "donkey_path.pkl"   # the path will be saved to this filename
diff --git a/apps/donkeycars/default_car/rpi-myconfig.py b/apps/donkeycars/default_car/rpi-myconfig.py
index 66cf1e2..89b43e2 100644
--- a/apps/donkeycars/default_car/rpi-myconfig.py
+++ b/apps/donkeycars/default_car/rpi-myconfig.py
@@ -1,13 +1,10 @@
 # """
 # My CAR CONFIG
-
 # This file is read by your car application's manage.py script to change the car
 # performance
-
 # If desired, all config overrides can be specified here.
 # The update operation will not touch this file.
 # """
-
 # import os
 #
 # #PATHS
@@ -26,7 +23,7 @@
 # IMAGE_DEPTH = 3         # default RGB=3, make 1 for mono
 # CAMERA_FRAMERATE = DRIVE_LOOP_HZ
 CAMERA_VFLIP = True
-# CAMERA_HFLIP = True
+CAMERA_HFLIP = True
 # # For CSIC camera - If the camera is mounted in a rotated position, changing the below parameter will correct the output frame orientation
 # CSIC_CAM_GSTREAMER_FLIP_PARM = 0 # (0 => none , 4 => Flip horizontally, 6 => Flip vertically)
 #
@@ -125,7 +122,7 @@
 # #line parameter --type to the python manage.py train and drive commands.
 # # tensorflow models: (linear|categorical|tflite_linear|tensorrt_linear)
 # # pytorch models: (resnet18)
-# DEFAULT_MODEL_TYPE = 'linear'
+DEFAULT_MODEL_TYPE = "categorical"
 # BATCH_SIZE = 128                #how many records to use when doing one pass of gradient decent. Use a smaller number if your gpu is running out of memory.
 # TRAIN_TEST_SPLIT = 0.8          #what percent of records to use for training. the remaining used for validation.
 # MAX_EPOCHS = 100                #how many times to visit all records of your data
@@ -321,7 +318,7 @@
 # AI_LAUNCH_KEEP_ENABLED = False      # when False ( default) you will need to hit the AI_LAUNCH_ENABLE_BUTTON for each use. This is safest. When this True, is active on each trip into "local" ai mode.
 #
 # #Scale the output of the throttle of the ai pilot for all model types.
-AI_THROTTLE_MULT = 0.85  # this multiplier will scale every throttle value for all output from NN models
+AI_THROTTLE_MULT = 1.65  # this multiplier will scale every throttle value for all output from NN models
 #
 # #Path following
 # PATH_FILENAME = "donkey_path.pkl"   # the path will be saved to this filename