BexTuychiev

{
  "league": {
    "name": "Premier League"
  },
  "clubs": [
    {
      "name": "Manchester United",
      "founded_year": 1878
    },
    {

BexTuychiev

# 1. Import statements and environment setup
import os
import random
import warnings
from typing import Callable, Tuple

import matplotlib.pyplot as plt
import neptune
import numpy as np
import tensorflow as tf

BexTuychiev

import os
import time
import warnings
import neptune

from dotenv import load_dotenv
from lightgbm import LGBMClassifier
from xgboost import XGBClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

BexTuychiev

import gymnasium as gym
import numpy as np
import matplotlib.pyplot as plt
from moviepy.editor import ImageSequenceClip

def create_environment(env_name='Taxi-v3', render_mode='rgb_array'):
    """Create and return a Gymnasium environment."""
    return gym.make(env_name, render_mode=render_mode)

def initialize_q_table(env):

BexTuychiev

# Please, run the following GitHub gist first: https://gist.github.com/BexTuychiev/00b665c256351a63cca0512b1cd6d220

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

# Read the CSV file
data = pd.read_csv("optimization_results.csv")

BexTuychiev

import seaborn as sns
import numpy as np
import pandas as pd
import time
from tabulate import tabulate  # pip install tabulate

np.random.seed(42)

# Load and prepare data
dataset_size = 20_000

BexTuychiev

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import warnings

warnings.filterwarnings("ignore")
np.random.seed(42)

# Load the data

BexTuychiev

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import warnings

warnings.filterwarnings("ignore")
np.random.seed(42)

# Load the data

BexTuychiev

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import warnings

warnings.filterwarnings("ignore")
np.random.seed(42)

# Load the data

BexTuychiev

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import warnings

warnings.filterwarnings("ignore")
np.random.seed(42)

# Load the data