1 Project Overview

In this project, you will develop AI agents to control the iconic character Mario in the classic game Super Mario Bros using the gym-super-mario-bros environment. The main objective is to implement at least two distinct AI algorithms/methods and compare their performance, strengths, and weaknesses in the context of playing the game.

You can undertake this project in teams of 2 that you select, if you are looking for a partner please reach out to your lab demonstrators by emails

1.1 Requirements

1.1.1 Gym-Super-Mario-Bros Environment Setup

• Set up the gym-super-mario-bros environment on your local machine or any designated platform, see 4.2.2 Environment Creation.

1.1.2 AI Algorithm Implementations

• Choose and implement at least two AI algorithms. You may wish to consider the following:

– Reinforcement Learning: Q-learning [3], TD(λ)
– Rule-Based AI: logic and heuristics.
– Monte Carlo Tree Search (MCTS)

You are welcome to use more advanced algorithms that utilise deep learning such as DQN’s [6] or Proximal Policy optimisation etc. but these are not covered in the unit and lab facilitators may not be able to assist with your implementations. These algorithms will also have to be referenced in your project report.

1.2 Final Project Report

To demonstrate your understanding of your implementations you will be required to write a final project report. Your report must conform to the following guidelines

• At least 3 pages
• No longer than 5 pages
• No smaller than size 12 font

You are allowed to add appendices with extra figures and words but these may not be marked. Your report should cover the following areas:

1.2.1 Analysis

• Analyze and contrast the performance of the chosen AI methods.

• Discuss their respective strengths, weaknesses, and suitability for playing Super Mario Bros.

1.2.2 Performance Metrics

You will notice that gym-super-mario-bros reward function assumes the objective of the game is to move as far right as possible. You are encouraged to come up with other performance and evaluation metrics for your agents. Novel and interesting metrics that you come up with will be rewarded.

1.3 Marks Distribution

AI Algorithm Implementations (40%)

   – Successful implementation of two or more AI methods (20%)
   – Code quality, readability, and efficiency (10%)
   – Integration with the gym-super-mario-bros environment (10%)

Report Comparison and Contrast (30%)

   – In-depth analysis of the algorithms’ strengths and weaknesses (15%)
   – Properly conducted experiments and results presentation (10%)
   – Effective comparison of AI methods (5%)

Performance Metrics and Visualization (20%)
   – Selection and definition of appropriate performance metrics (10%)

   – Quality of visualization techniques (10%)

Report Quality (10%)

   – Clarity and organization of the report (5%)
   – Overall presentation and writing quality (5%)

1.4 Additional Notes

• You are encouraged to use external libraries and frameworks to support your implementation, but remember to provide proper citations and acknowledgments.

• Regularly check in with the instructor for progress assessments and guidance during the project timeline.

• Ensure that your work is original and properly referenced to maintain academic integrity throughout the project.