Minesweeper using Reinforcement Learning¶Import Libraires¶In [1]: import pandas as pdimport numpy as npfrom itertools import productimport randomfrom random import choicefrom collections import namedtuplefrom scipy.signal import convolve2dfrom tqdm import trangefrom time import sleepimport matplotlib.pyplot as pltfrom IPython.display import clear_outputimport ipywidgets as widgets%matplotlib..

1. Game ruleThere are 100 cardsTwo playersplayer-0 (AI), player-1 (human)Player-turn sequence: player0-player1-player0-player1- ….Each player will draw up to 3 cards in his turnThe player who draws the 100th card (the last card) wins! 2. Gameplay# of drawn cards = 0, Player-0 draws 3 cards# of drawn cards = 3, Player-1 draws 1 card# of drawn cards = 4, Player-0 draws 1 card# of drawn cards = 5, ..

1. DP using greedyfrom pyamaze import maze, agentimport numpy as np# Load the Mazesize = 5m=maze(size,size)m.CreateMaze(loadMaze="maze.csv")# create the environment modelstates = list(m.maze_map.keys())actions = ['E','N', 'W', 'S']# define how an action changes a statedef step(state, action): x, y = state if action=='E': y += 1 elif action=='W': y -= 1 elif action=='N':..

Import Library¶In [1]: import numpy as npfrom sklearn.datasets import load_breast_cancerfrom sklearn.decomposition import PCAimport matplotlib.pyplot as pltimport tensorflow as tf%matplotlib inline /home/pmi-minos-3090-single/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and ={np_minversion} and Data Load¶In [2]: data = loa..