Skip to content

Latest commit

 

History

History
104 lines (78 loc) · 3.41 KB

README.md

File metadata and controls

104 lines (78 loc) · 3.41 KB
Raw

CS 440 - Introduction to Artificial Intelligence

In this repo are all the assignments I've worked on over the semester for CS 440.

The most notable content in this repo is my Conway's Game of Life and Reinforcement Learning AI package--cgolai. This package was created to assist with the final project.

Final Project Details:

  • Package: src/cgolai
  • Analysis Files: proj/run_report.py
  • Final Project Report: Report.ipynb
  • References Summary (per Honors Option Requirement): ReportWritingComponent.docx
  • See Final Project

Assignments:

  • Ininformed Search
  • Iterative-Deepening Search
  • A*, IDS and Effective Branching Factor
  • Reinforcement Learning (Hanoi, Q-function, SARSA)
  • Neural Networks
  • Min-Conflicts

Final Project

Reinforcement Learning to control Conway's Game of Life

Summary: I created a reinforcement agent capable of learning to control a mutated form of Conway's Game of Life (Cgol). My package, cgolai, contains a built Cgol GUI, PyTorch Nueral Network, and Deep RL agent with Neural Network as Q-function. See Report.ipynb for more information.

In this context, the "actions" the RL agent performs is reviving or eliminating cells within the grid at any iteration. This is different from the original in that the original game's grid can only be changed at the start. Further, the Cgol grid is actually a toroid (going off the left of the screen puts you on the right side). This is to limit the complexity since infinite grids are not efficient.

Results: The RL agent typically generated still-life and then avoided interacting with the system.

Using cgolai

cgolai can be installed, or you can copy the project directory from src to wherever you need it (be sure to check dependencies in setup.py if you do this. I recommend using a virtual environment.

Notable components are the following:

  • cgolai.ai.NNTorch Neural Network class
  • cgolai.ai.ProblemNN Problem definition class (used by RL agent)
  • cgolai.ai.RL RL Agent with Neural Network as Q-function
  • cgolai.ai.RLQ RL Agent with dictionary as Q-function
  • cgolai.cgol.Model The headless Cgol Model
  • cgolai.cgol.Cgol The class for the Cgol GUI (See Running Cgol AI GUI)
  • cgolai.cgol.CgolProblem The problem definition of Cgol (used by RL agent)

Running Cgol AI GUI

python3 -m cgolai.cgol [options]

Options

  • -c will print the controlls to control the GUI.
  • -v will print information to the console (mostly debugging information)
  • -f {save file} the save file will be loaded if it exists.
  • -w {grid width} width of Cgol toroid grid
  • -h {grid height} height of the Cgol toroid grid

Directory Layout

  • Assignments are stored in the assign directory. Each has a Jupyter Notebook for easy viewing.
  • Source code for my final project is stored in src and proj.
  • src contains the package I built to assist with the experimentation (including the Neural Network, RL agent, Cgol model, and Cgol GUI).
  • proj contains the testing and experimentation I did to generate the results summarized in the report.
  • MANIFEST.in, Makefile, and setup.py are used for the package.

Installing cgolai

git clone https://github.com/larkwt96/cgolai.git
cd cgolai
python3 -m pip install .

or without cloning

python3 -m pip install git+git://github.com/larkwt96/cgolai.git

Testing

python3 -m unittest discover -s src