https://github.com/misterzurg/itmo_evolutionary_computing

🧬 Labs from ITMO; Dis - EC
https://github.com/misterzurg/itmo_evolutionary_computing

big-data-and-ml evolutionary-computation itmo-labs

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🧬 Labs from ITMO; Dis - EC

• Host: GitHub
• URL: https://github.com/misterzurg/itmo_evolutionary_computing
• Owner: MisterZurg
• Created: 2023-11-18T15:58:31.000Z (about 1 year ago)
• Default Branch: dungeon-master
• Last Pushed: 2024-01-31T18:42:40.000Z (11 months ago)
• Last Synced: 2024-01-31T19:40:44.685Z (11 months ago)
• Topics: big-data-and-ml, evolutionary-computation, itmo-labs
• Language: Jupyter Notebook
• Homepage:
• Size: 16 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
> [!NOTE]

> Here are our labs from ITMO Third semester

# Discipline

Evolutionary Computing

## Instructors

[Mikhail Melnik](https://github.com/HighExecutor) Associate Professor of Digital Transformation

## Labs

### Lab 1 Introduction to evolutionary computation

> Goal is to edit Genetic Algorithm setup in such a way that the algorithm would be able to solve classic optimization function (shifted reversed Rastrigin function). 

> You need to set dimension to 100. Don't forget this. This will increase complexity of function.

> The maximum value that you can achieve is 10.0, but lets say you want to achieve results with quality > 9.5.

### Lab 2 Designing an evolutionary algorithm for the queen placement problem 

> Implement solving of Queens Puzzle with evolutionary algorithm. For example, [genetic algorithm](https://en.wikipedia.org/wiki/Eight_queens_puzzle).

> The goal is to design algorithm that would solve queens puzzle with at least 8x8 board. But you can try bigger sizes. As a pattern you can use the same script as for 1st assignment, but modify all required things to work with discrete problem. It is > important to design how you will encode solution (how you represent a solution in a code)  and design correspond mutation\crossover operators. 

> Try to print in console your best found solution at the end of your script.

### Lab 3 CartPole Left Right.

> You need to change classic CartPole environment in such a wat that it will have a goal (0 or 1) and Cart should balance the Pole in correspond sides of platform (0 - left, 1 - right). The goal implementation is already provided for you. The main goal is to implement reward function that will helps agent to do this.

> 

> 1. You can find a template project with scripts for :

>    - CartPoleEnv with implemented goal (left or right)

>    - Train script with RLLib

>    - Script for replaying rllib checkpoint

> 2. You need to implement reward function in CartPoleEnv script in such a way that your Cart will balance Pole on specific side of platform depending on the goal. For example if goal is 0 then Pole must be balances on left side. There is no hard restirictions for how far it should be, but far is better (in range -2.4 to 2.4)

> 3. You need to train it with RLLib (template is given) and send me a simple report with plots from tensorboard (generated by RLLib) and video with your result. (Demo is attached here).

## Refs

- [Manual for labs](https://books.ifmo.ru/file/pdf/2389.pdf) kinda

- [GitLab with some Labs](https://gitlab.com/itmo_ec_labs)