Homework 5

starter code

In this homework, we will train a CNN to do vision-based driving in SuperTuxKart.

This assignment should be solved individually. No collaboration, sharing of solutions, or exchange of models is allowed. Please, do not directly copy existing code from anywhere other than your previous solutions, or the previous master solution. We will check assignments for duplicates. See below for more details.

We will design a simple low-level controller that acts as an auto-pilot to drive in SuperTuxKart. We then use this auto-pilot to train a vision based driving system. To get started, first download and install SuperTuxKart on your machine. If you are working on colab, be sure that you have the GPU hardware accelerator enabled. To enable the GPU hardware accelerator go to Runtime > Change Runtime Type > GPU > Save your colab notebook will then restart with GPU enabled.


Once you have GPU enabled use the following to install SuperTuxKart:

%pip install -U PySuperTuxKart

When running the simulator, if you encountered errors about irrlicht devices, make sure you have EGL installed. You can install EGL using conda install -c anaconda mesa-libegl-cos6-x86_64 if you are using conda.

If installing on a Windows machine it is strongly suggested that you install PySuperTuxKart in a Anaconda environment. Anaconda will handle all of the compilation details that can be quite challenging on a Windows machine. To accomplish this: first create a conda environment for hw5, install the dependencies listed in environment.yml (except PySuperTuxKart as it is not available through Anaconda), and finally install PySuperTuxKart using pip as shown above.

If you encounter any issues installing this package, please post them in Piazza.


In the first part of this homework, you will write a low-level controller in controller.py. The controller function takes as input an aim point and the current velocity of the car. The aim point is a point on the center of the track 15 meters away from the kart, as shown below.


We use screen coordinates for the aim point: [-1..1] in both x and y directions. The point (-1,-1) is the top left of the screen and (1, 1) the bottom right.

In the first part of this assignment, we will use a ground truth aim point from the simulator itself. In the second part, we remove this restriction and predict the aim point directly from the image.

The goal of the low-level controller is to steer towards this point. The output of the low-level controller is a pystk.Action. You can specify:

Implement your controller in the control function in controller.py. You don’t need any deep learning to design this low-level controller. You may use numpy instead of pytorch if you wish.

Once you finish, you could test your controller using

python3 -m homework.controller [TRACK_NAME] -v

You should tune the hyper-parameters of your controller. You may want to consider gradient-free optimization or exhaustive search. The reference controller completes each level relatively efficiently: zengarden and lighthouse in under 50 sec, hacienda and snowtuxpeak in under 60 sec, cornfield_crossing and scotland in under 70 sec. Note that these times are in-game times and will generally be less than the total computational runtime.

Grade your controller using

python3 -m grader homework

Hint: Skid if the steering angle is too large.

Hint: Target a constant velocity.

Hint: Steering and relative aim point use different units. Use the aim point and a tuned scaling factor to select the amount of normalized steering.

Hint: Make sure that your controller is able to complete all levels before proceeding to the next part of the homework because you will use your controller to build the training set for your planner.


In the second part, you will train a planner to predict the aim point. The planner takes as input an image and outputs the aim point in the image coordinate. Your controller then maps those aim points to actions.


Use your low-level controller to collect a training set for the planner.

python3 -m homework.utils zengarden lighthouse hacienda snowtuxpeak cornfield_crossing scotland

We highly recommend you limit yourself to the above training levels, adding additional training levels may create an unbalanced training set and lead to issues with the final test_grader.

This function creates a dataset of images and corresponding aim points in drive_data. You can visualize the data using

python3 -m homework.visualize_data drive_data

Below are a few examples from the master-solution controller.



Implement your planner model in Planner class of planner.py. Your planner model is a torch.nn.Module that takes as input an image tensor and outputs the aiming point in image coordinates (x:0..127, y:0..95). We recommend using an encoder-decoder structure to predict a heatmap and extract the peak using a spatial argmax layer in utils.py. Complete the training code in train.py and train your model using python3 -m homework.train.

Vision-Based Driving

Once you completed everything, use

python3 -m homework.planner [TRACK_NAME] -v

to drive with your CNN planner and controller.

The red circle in the image below is being predicted using the trained master-solution planner network as a substitute for the ground truth aim point used previously.



We will grade both your controller and planner on the following 6 tracks

Your controller/planner should complete each track within a certain amount of time (see grader for details). You receive 5% of your grade by completing each track with your low-level controller. You receive 10% of your grade by completing each track with your image-based agent (i.e., planner/controller pair). You may train on all the above testing track.

For the last 10%, you’ll need to complete an unseen test track. We chose a relatively easy test track. You can test your solution against the grader by

python3 -m grader homework

Extra credit (up to 10pt)

We will run a little tournament with all submissions, the top 9 submissions will receive 10, 9, 8, … extra credit respectively. The tournament uses several unreleased test tracks.


Once you finished the assignment, create a submission bundle using

python3 bundle.py [YOUR UT ID]

and submit the zip file online. If you want to double-check that your zip file was properly created, you can grade it again

python3 -m grader [YOUR UT ID].zip


The test grader we provide

python3 -m grader homework -v

will run a subset of test cases we use during the actual testing. The point distributions will be the same, but we will use additional test cases. More importantly, we evaluate your model on the test set. The performance on the test grader may vary. Try not to overfit to the validation set too much.


Once you finished the assignment, create a submission bundle using

python3 bundle.py homework [YOUR UT ID]

and submit the zip file on canvas. Please note that the maximum file size our grader accepts is 20MB. Please keep your model compact. Please double-check that your zip file was properly created, by grading it again

python3 -m grader [YOUR UT ID].zip

Online grader

We will use an automated grader through canvas to grade all your submissions. There is a soft limit of 5 submisisons per assignment. Please contact the course staff before going over this limit, otherwise your submission might be counted as invalid.

The online grading system will use a slightly modified version of python and the grader:

Please do not try to break or hack the grader. Doing so will have negative consequences for your standing in this class and the program.

Your latest submission always overrides any previous attempt and counts to wards your final grade (this includes late days).

Running your assignment on google colab

This assignment requires a slightly different setup than the previous colab starters.
IMPORTANT: download this ipynb.

Next, upload the notebook to http://colab.research.google.com then follow the instructions in the notebook.

Honor code

This assignment should be solved individually.

What interaction with classmates is allowed?

What interaction is not allowed?

Ways students failed in past years (do not do this):

Installation and setup

Installing python 3

Go to https://www.python.org/downloads/ to download python 3. Alternatively, you can install a python distribution such as Anaconda. Please select python 3 (not python 2).

Installing the dependencies

Install all dependencies using

python3 -m pip install -r requirements.txt

Note: On some systems, you might be required to use pip3 instead of pip for python 3.

If you’re using conda use

conda env create environment.yml

The test grader will not have any dependencies installed, other than native python3 libraries and libraries mentioned in requirements.txt. This includes packages like pandas. If you use additional dependencies ask on piazza first, or risk the test grader failing.

Manual installation of pytorch

Go to https://pytorch.org/get-started/locally/ then select the stable Pytorch build, your OS, package (pip if you installed python 3 directly, conda if you installed Anaconda), python version, cuda version. Run the provided command. Note that cuda is not required, you can select cuda = None if you don’t have a GPU or don’t want to do GPU training locally. We will provide instruction for doing remote GPU training on Google Colab for free.

Manual installation of the Python Imaging Library (PIL)

The easiest way to install the PIL is through pip or conda.

python3 -m pip install -U Pillow

There are a few important considerations when using PIL. First, make sure that your OS uses libjpeg-turbo and not the slower libjpeg (all modern Ubuntu versions do by default). Second, if you’re frustrated with slow image transformations in PIL use Pillow-SIMD instead:

CC="cc -mavx2" python3 -m pip install -U --force-reinstall Pillow-SIMD

The CC="cc -mavx2" is only needed if your CPU supports AVX2 instructions. pip will most likely complain a bit about missing dependencies. Install them, either through conda, or your favorite package manager (apt, brew, …).