Improved running instructions in root README. Upgraded requirements.txt. Fix for Chapter 10 code.

Author: Rishal Hurbans

README.md

@@ -1,4 +1,4 @@
-# Grokking Artificial Intelligence Algorithms
+# Grokking AI Algorithms
 
 [Get Grokking Artificial Intelligence Algorithms at Manning Publications](https://www.manning.com/books/grokking-artificial-intelligence-algorithms?a_aid=gaia&a_bid=6a1b836a) 
 
@@ -7,30 +7,37 @@ Rather Learn by exploring the code notebook in your browser? Click here:
 [![Open the interactive code notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rishal-hurbans/Grokking-Artificial-Intelligence-Algorithms-Notebook/blob/main/Grokking_Artificial_Intelligence_Algorithms_Notebook.ipynb)
 
 ## Requirements
-* Python 3.7.0+
-* Pip 3
+* Python 3.9 or later (3.11 recommended)
+* pip 23.1+ (comes with recent Python installers)
+* Optional: PyTorch-compatible GPU for the heavy demos in Chapters 11–12
 
 ## Setup
-Make sure that you have Python 3.7.0+ installed. [Download Python here](https://www.python.org/downloads/).
-pip3 should be installed with Python 3.7.0+ on macOS and Windows. You may need to install pip3 seperately if you're using Linux.
-Use the ```sudo apt-get install python3-pip``` command if you're using Ubuntu or Debian Linux.
-Use the ```sudo yum install python3-pip``` command if you're using Fedora Linux. 
-
-Clone this repository.
-
-Navigate to the directory that contains the repository using terminal/console/command line.
-
-Run this command to install the dependency libraries required.
-
-```pip3 install -r requirements.txt```
-
-Navigate to the directory containing the example that you want to run, and run this command.
-
-```python3 <file_name.py>```
-
+1. **Install Python** – download the latest 3.x release from [python.org](https://www.python.org/downloads/) or use your platform package manager. Ensure `python` and `pip` point to the same interpreter (`python -m pip --version`).
+2. **Create a virtual environment** (recommended so project dependencies stay isolated):
+   * macOS / Linux:
+     ```bash
+     python3 -m venv .venv
+     source .venv/bin/activate
+     ```
+   * Windows (PowerShell):
+     ```powershell
+     py -3 -m venv .venv
+     .\.venv\Scripts\Activate.ps1
+     ```
+3. **Install dependencies**:
+   ```bash
+   pip install --upgrade pip
+   pip install -r requirements.txt
+   ```
+   *PyTorch wheels are large; on Apple Silicon use the `arm64` build from `pip` or follow the [official instructions](https://pytorch.org/get-started/locally/) if you need CUDA support.*
+4. **Run an example** by moving into the chapter directory and executing the script:
+   ```bash
+   cd ch03-intelligent_search/informed_search
+   python3 maze_astar.py
+   ```
 
 ## Overview
-This is the official supporting code for the book, Grokking Artificial Intelligence Algorithms, published by Manning Publications, authored by Rishal Hurbans.
+This is the official supporting code for the book, Grokking AI Algorithms, published by Manning Publications, authored by Rishal Hurbans.
 
 ![History of AI](readme_assets/history_of_ai.png)
 
@@ -39,4 +46,3 @@ The example implementations provided will make more sense if you've read the boo
 The purpose of this repository is to act as a practical reference for examples of how the algorithms mentioned in the book can be implemented.
 This repository should not be consulted as the book is read page-by-page, but rather, when you're attempting to implement an algorithm or gain a more technical understanding from a programming perspective.
 
-

ch10-reinforcement_learning/auto_simulator.py

@@ -244,7 +244,7 @@ def execute_random_brute_force():
 def train_with_q_learning(observation_space, action_space, number_of_iterations, learning_rate, discount,
                           chance_of_random_move):
     # Initialize the Q-table
-    q_table = np.zeros([observation_space, action_space], dtype=np.int8)
+    q_table = np.zeros([observation_space, action_space], dtype=np.int32)
 
     # Repeat for a number of iterations
     for i in range(number_of_iterations):

requirements.txt

@@ -1,21 +1,31 @@
-cycler==0.10.0
-entrypoints==0.3
-flake8==3.7.9
-joblib==0.14.1
-kiwisolver==1.2.0
-matplotlib==3.7.1
-mccabe==0.6.1
-numpy==1.18.4
-pandas==1.0.3
-pycodestyle==2.5.0
-pydotplus==2.0.2
-pyflakes==2.1.1
-pyparsing==2.4.7
-python-dateutil==2.8.1
-pytz==2020.1
-scikit-learn==0.22.2.post1
-scipy==1.4.1
-seaborn==0.10.1
-six==1.14.0
-sklearn==0.0
-torch
+# Core scientific stack
+numpy>=1.26,<3
+scipy>=1.11
+pandas>=2.2
+matplotlib>=3.8
+seaborn>=0.13
+
+# Machine learning
+scikit-learn>=1.4
+joblib>=1.3
+
+# Utilities
+python-dateutil>=2.8
+pytz>=2024.1
+cycler>=0.11
+kiwisolver>=1.4
+pyparsing>=3.1
+six>=1.16
+
+# Optional linting tools
+flake8>=7.0
+mccabe>=0.7
+pycodestyle>=2.11
+pyflakes>=3.2
+
+# Deep learning (required for Chapters 9, 11, 12)
+torch>=2.3
+
+# Visualization helpers
+entrypoints>=0.4
+pydotplus>=2.0.2