Chapter 2 – Inside the Agent Brain: UI Gym, Q-Learning Using Tic-Tac-Toe | E-commerce AI Foundations

Описание: Whats coming next in A100 GPUs

Chapter 3: Browser agents — Playwright + Python
Chapter 4: E-Commerce purchase ai assistants — GPUs, cost, and reality
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if you are interested chapter 1 link here    • Chapter 1–Building basic UI Gym for Reward...  
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In Chapter 2 of this series, we go inside the creating UI gym for brain of a Reinforcement Learning agent.

We’re not just playing Tic-Tac-Toe for fun.
This is the same foundational logic used by real-world E-commerce and browser automation agents.

You’ll see how a simple game exposes the core mechanics behind agents that:

Navigate complex UIs

Avoid illegal actions

Learn from delayed rewards

Scale from laptops to A100-class infrastructure

What we break down in this video:

State Space
Why Tic-Tac-Toe has 3⁹ possible states — and why state explosion is the real enemy in UI automation.

The Bellman Equation (Q-Learning)
How rewards propagate backward through time using Gamma (γ) — the same math behind long-horizon decision making in web agents.

Action Masking
How to stop your AI from making stupid, illegal moves — a critical concept for real browser and E-commerce agents.

This is a line-by-line walkthrough:

From self.q_table

To the update rule

To production-grade agent behavior

Whether you’re:

A beginner trying to actually understand RL

Or an advanced engineer preparing for A100-scale UI agents

This chapter builds the mental model you must have before scaling.

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#ReinforcementLearning
#QLearning
#AIAgents
#UIGym
#EcommerceAI
#Python
#MachineLearning
#AIEngineering
#BrowserAutomation