Project Blog

Mar 14, 2017

  • TBD

Mar 7, 2017

  • Showed the modified Q learning algorithm
  • Professor suggested to input the rewards and states from the screenshots of the image
  • To track the reward (a player's lifeline), map non blue or non yellow to the background color
  • Look for pixel pattern using OpenCV and compute the current state of the player, i.e., the coordinates of its position on the screen

Feb 28, 2017

  • Implemented a Q learning algorithm
  • Professor recommended to modify the algorithm such that it plays hardcoded policies

Feb 21, 2017

  • Showed the first deliverable (a bot that plays Archon on Commadore 64 emulator)
  • Certain changes were recommended (make it play with 5x speen) in the first deliverable by the professor
  • Discussed the work needed to be done for the second deliverable

Feb 14, 2017

  • Discussed about the different libraries that can be used to make the program control keyboard and mouse events, and why the libraries decided previously weren't working with the keyboard
  • Selected Commadore 64 as the emulator to play Archon

Feb 7, 2017

  • Discussed the schedule for CS 298, deliverables and their due dates
  • Discussed the strategy for the first deliverable

Dec 8, 2016

  • Showed deliverble 3 after the required changes, used another library called TensorFlow to improve the results
  • A few more changes were suggested by the professor and approach for deliverable 4 was discussed
  • The first draft of the report was discussed and a few changes were suggested by the professor

Nov 29, 2016

  • Discussed the suggested modifications in deliverable 3 (using a python library called Theano)
  • The model wasn't correctly able to predict the digits after a screenshot was taken of the image and then passed to it. A few changes were suggested by the professor

Nov 22, 2016

  • Discussed the code for Deliverable 3 and a methodology was suggested by the professor to save the model in a pickle file instead of running the model everytime
  • Discussed how different layers could be added to make the model efficient and make correct predictions and the working of Theano

Nov 15, 2016

  • Added a few more convolutional layers to the simple multi layer network and increased the epochs to make the model stronger
  • Discussed how a neural network could be used to approximate a Q function

Nov 8, 2016

Nov 1, 2016

  • Presented the 3rd Deliverable , a simple multi layer AI using THEANO
  • The Optical Character Recognition system was trained using deep convolutional networks to solve the problem classifying handwritten digits from the MNIST dataset (taken from Kaggle)
  • Reviewed code

Oct 25, 2016

Oct 18, 2016

  • Power point presentation and discussion on "Reinforcement Learning", from Artificial Intelligence: A Modern Approach

Oct 11, 2016

  • Presented the 2nd Deliverable , a python program that controls the movements of the keyboard and mouse
  • The purpose of this deliverable was to implement a program that can play Archon (on its own) or any other classic game while taking screen shots
  • Reviewed code

Oct 04, 2016

  • Finish reading "Making Complex Decisions" and "Reinforcement Learning"
  • Power point presentation and discussion on "Making Complex Decisions", from Artificial Intelligence: A Modern Approach

Sep 27, 2016

  • Read "Making Complex Decisions" and "Reinforcement Learning" (chapter 17 and 21 respectively) from Artificial Intelligence: A Modern Approach
  • Design powerpoint slides summarizing the concepts learnt in them.

Sep 20, 2016

  • Added more details to the python program developed for the second deliverable
  • The program now takes a screenshot of a particular part of the screen.
  • It downscales the images stored and stores them into a folder every one tenth of a second.

Sep 13, 2016

  • Start reading the research papers, "Evolving Neural Networks through Augmenting Topologies", "Playing Atari with Deep Reinforcement Learning"
  • Design powerpoint slides summarizing the concepts learnt in them.
  • Presented the 1st Deliverable , a python program that takes screen shots of the screen
  • Reviewed code

Sep 6, 2016

  • Professor Pollett reviewed the CS 297 project proposal
  • Discussed the initial steps of the problem, and then relevant research papers, youtube video tutorials were assigned.
  • Design a program that takes a screenshot.
  • Refer videos like: ""