Q_learning

Important Links

Google Drive: https://drive.google.com/drive/folders/10fpWJJ92SCjZ-gV1rOac0aVbyJg_O_dH?usp=sharing
Repository: https://github.com/YashBansod/CMSC_818B_Decision_Making_in_Robotics/Q_learning

Some key highlights about the source code:

• The source code in this project is 100 % PEP-8 compliant.

Run Instructions

• Open the terminal.
• If you are accessing this project from github, clone it onto your system.
  git clone https://github.com/YashBansod/CMSC_818B_Q_learning
• Download the saved_models folder from the google drive link and place the folder in this project’s directory.
• If you have a python virtual environment, activate it. Else you will be using the system default python environment.
• In the terminal, browse to the directory containing this project.
• Install the requirements of this project. pip install -r requirements.txt
• Run the problem1_sol.py as python file.
  python problem1_sol.py -t -v -d

Note: Run the help option of the python file to see all argument options. python problem1_sol.py -h

Note: You may have to add the project directory to the PATH environment variable.
For linux:
export PYTHONPATH=$PYTHONPATH:<absolute path to project directory>

For windows:
set PYTHONPATH=%PYTHONPATH%;<absolute path to project directory>

Developed on:

• Windows 10 (version 1903)
• Pycharm Professional 2019.2
• Python 3.7.3
  Note: I have written the code keeping in mind about the back compatibility with Python 2.7. However, I have tested it on Python 3.7.3 only. If you find any difficulties in executing the code on any other version, try using a virtual interpreter of python 3.7.3 or report the error to me for resolution.

Results

Solved Mountain Car Problem:

Policy Visualization for Problem 1 (Unmodified Reward) (Experiment 2 in writeup):

Policy Visualization for Problem 1 (Unmodified Reward) (Experiment 1 in writeup):

Author: Yash Bansod
UID: 116776547
E-mail: yashb@umd.edu
Organization: University of Maryland, College Park