In Lab_01:
In this we have implement two methods –Gradient Descent method and Newton’s method for function minimalization(please, be aware that the user should have a possibility to select which method will be takenfor function minimalization). With case of stopping conditions { •Maximum number of iterations •Desiredvalue 𝐹(𝑥)or 𝐺(𝑥)to reach(so the processis finished when 𝐹(𝑥)≥𝑣𝑎𝑙𝑢𝑒_𝑡𝑜_𝑟𝑒𝑎𝑐ℎ/ 𝐺(𝑥)≥𝑣𝑎𝑙𝑢𝑒_𝑡𝑜_𝑟𝑒𝑎𝑐ℎ) •Maximum computation time }
In Lab_02:
In this we have implementation must have the following componentsand fulfil the following requirements: •Roulette-wheel selection with scaling •Single point crossover •FIFO replacement strategy •Genetic algorithm must use binary vectors The user should also have a possibility to specify diversified parameters of the algorithm. They are as follows: •The problem dimensionality •The range of searched integers as 𝑑≥1that for each dimension i, −2^d≤𝑥(i)<2^d •Function parameters A, b, c •The algorithm parameters as: population size, crossover probability, mutation probability, number of algorithms iterations
In Lab_03:
Program that plays tic-tac-toe with the user on a 3×3board. The game continues until one of the players wins or it is a draw. The first player to get 3 of his/her marks in a row (up, down, across, or diagonally)wins.
Algorithm: min-max with alpha-beta pruning.
Lab_04:
Solve problems using Prolog.
Convert an input number N to English words. N <= 1000
lab_05:
Create an implementation of the Q-Learning algorithm to solve a toy Reinforcement Learning problem. Use the environment provided from OpenAI gym library. The original gym library is no longer updated, however, there is a continued development on a fork of gym called gymnasium. We use gymnasium for this exercise. Use the following environments for each lab variants:
Taxi. Use "Taxi-v3"FrozenLake. Use the arguments "FrozenLake-v1" and map_name="8x8" for gym.make()