American University of Sharjah Artificial Intelligence Discussion

Question Description

All instructions are in the provided zip file, unzip and go to the file reinforcement.html. Click it and it should open in a browser. Read the instructions carefully. Detailed instructions are provided in each linked python file as comments. You will need Python 2.7. I recommend to install a conda environment like so:

conda create -n relearn python=2.7conda activate relearn

Please provide all the files requested to be changed + a report written in LaTeX (Overleaf)

Here is a small description from reinforcement.html:-

In this project, you will implement value iteration and q-learning. You will test your agents first on Gridworld (from class), then apply them to a simulated robot controller (Crawler) and Pac-Man.

The code for this project contains the following files, which are available in a zip archive:

Files you will edit

`valueIterationAgents.py`	A value iteration agent for solving known MDPs.
`qlearningAgents.py`	Q-learning agents for Gridworld, Crawler and Pac-Man
`analysis.py`	A file to put your answers to questions given in the project.

Files you should read but NOT edit

`mdp.py`	Defines methods on general MDPs.
`learningAgents.py`	Defines the base classes `ValueEstimationAgent` and `QLearningAgent`, which your agents will extend.
`util.py`	Utilities, including `util.Counter`, which is particularly useful for q-learners.
`gridworld.py`	The Gridworld implementation
`featureExtractors.py`	Classes for extracting features on (state,action) pairs. Used for the approximate q-learning agent (in qlearningAgents.py).

Files you can ignore

`environment.py`	Abstract class for general reinforcement learning environments. Used by `gridworld.py`.
`graphicsGridworldDisplay.py`	Gridworld graphical display.
`graphicsUtils.py`	Graphics utilities.
`textGridworldDisplay.py`	Plug-in for the Gridworld text interface.
`crawler.py`	The crawler code and test harness. You will run this but not edit it.
`graphicsCrawlerDisplay.py`	GUI for the crawler robot.

What to submit: You will fill in portions of valueIterationAgents.py, qlearningAgents.py, and analysis.py during the assignment. You should submit only these files. Please don’t change any others.

Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation — not the autograder’s judgements — will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.

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