ROAR-Academy

ROAR Academy was created by Dr. Allen Y. Yang. The material has been taught at the University of California, Berkeley, as part of Berkeley ROAR Racing curriculum to students who want to learn introduction-level skills about:

• Python Programming;
• Scientific Programming using NUMPY
• Gradient-Descent Algorithms
• Deep Neural Networks (DNN)

The material has been made ope source FOR NON-COMMERCIAL USE only. Please contact the author for any questions regarding commercial licensing: allen@intelligentracing.com

Basic Installation

Users of this course are recommended to install the following software packages. The packages have been tested on Windows 10 and above, Mac OSX, and Ubuntu Linux systems.

• Python 3.8 via Miniconda (conda): https://docs.conda.io/en/latest/miniconda.html
• Git: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git
• Visual Studio Code (code): https://code.visualstudio.com/download

NOTE: If you are running conda and code in Windows, launching code from Anaconda Prompt will make sure that the conda environment available inside the integrated Terminal window. Otherwise you may encounter error that conda is not installed

Once Python 3.8 is installed, the following Python modules can be installed using pip within the conda environment (for example, in (base) environment):

    python -m pip install numpy matplotlib

Tensorflow Installation for Windows and Linux

For the DNN portion, we use tensorflow 2 and keras. The installation shall reference the official documentation: https://www.tensorflow.org/install.

Due to the fact that Google stopped supporing Windows Native environment above tensorflow 2.10, please make sure to install and use tensorflow 2.10 for compatibility with our tested code

If your PC comes with supported NVidia GPU for accelerating DNN code, please follow carefully the setup of GPU support for Linux and Windows Native. For MacOS, please read the instruction at the end

After you have setup your system for an installation on either CPU or GPU, run the following pip script to install tensorflow 2.10

    python -m pip install --upgrade pip
    python -m pip install tensorflow==2.10

Finally, you may verify tensorflow has been properly set up by running the following test

    python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

If you have install tensorflow with GPU support, you may verify by running the following test

    python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Tensorflow Installation with Metal accelerated backend for MacOS

Apple has released a Metal-accelerated tensorflow backend to utilize the GPU on MacOS. Please visit Apple website to install tensorflow 2.10: https://developer.apple.com/metal/tensorflow-plugin/

Installation of Reinforcement Learning Modules

This course will require installation of the following reinforcement learning modules:

• gym simulator: https://github.com/openai/gym

python -m pip install gym==0.17.3
python -m pip install pyglet==1.5.11

• stable-baselines 3: https://github.com/DLR-RM/stable-baselines3

    python -m pip install stable-baselines3[extra]

• highway-env: https://github.com/Farama-Foundation/HighwayEnv

    python -m pip install highway-env