CS285

Tensorflow Version of homework assignments of Deep Reinforcement Learning Course
Presented by Dr. Sergey Levin at University of California, Berkeley

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Table of Contents

• About the Project
• Getting Started
• Usage
• Roadmap
• License
• Contact

About The Project

In this project, we aim to solve the assignments of CS285 course.

Getting Started

Unfortunately, the current version of this repository is not compatible with the latest versions of libraries, such as Tensorflow and Mojocu-py. As a result, installing the proper versions of these libraries, which can enable you to contribute to this repo, could be a hard challenge. However, since I have been faced with this problem before, I designed a certain number of steps that you can take to install the right versions of these libraries.

1. Create a new Conda environment based on Python 3.5 and install matplotlib, ipython, and pytorch. Then, activate it.

conda create -n cs285_env python=3.5 matplotlib ipython pytorch=1.5.0
source activate cs285_env

2. Clone this repository
3. Install mujoco-py
   1. Get mujoco license key file from its website
   2. Create a .mujoco folder in the home directory and copy the given mjpro150 directory and your license key into it

   mkdir ~/.mujoco/
   cd <location_of_your_license_key>
   cp mjkey.txt ~/.mujoco/
   cd <this_repo>/mujoco
   cp -r mjpro150 ~/.mujoco/

   3. Add the following line to bottom of your .bashrc file:

   export LD_LIBRARY_PATH=~/.mujoco/mjpro150/bin/

   4. Build and install mujoco-py 1.50.1.1. It can be downloaded from this link.

   tar -xzf mujoco-py-1.50.1.1.tar.gz 
   cd mujoco-py-1.50.1.1
   python setup.py install

4. Install rest of the libraries given in contribution_requirements.txt file using pip

pip install --user --requirement contribution_requirements.txt

4. At last, it should be considered that before executing scripts of each homework folder (e.g., hw1), you should allow your code to be able to see 'cs285' by executing the following lines:

cd <path_to_hw>
pip install -e .

Usage

The instructions for execution of all of these assignments are given in the Readme documents that are located in each of the homework directories.

Roadmap

See the open issues for a list of known issues.

License

Distributed under the MIT License. See LICENSE file for more information.

Contact

Erfan Miahi - @erfan_mhi - miahi@ualberta.com

Project Link: https://github.com/erfanMhi/Deep-Reinforcement-Learning-CS285-Pytorch