Q-Learning Tutorial 🧠

Q-Learning is a model-free reinforcement learning algorithm used to learn the optimal policy in environments without explicit knowledge of the model. It's widely applied in robotics, game AI, and autonomous systems. Let's break it down:

Key Concepts

  • Q-Table: Stores expected rewards for state-action pairs
  • Bellman Equation: Core formula for updating Q-values
  • Exploration vs Exploitation: Balance between trying new actions and using known ones
  • Discount Factor (γ): Controls future reward importance

Algorithm Flow

  1. Initialize Q-table with zeros
  2. For each episode:
    • Observe current state s
    • Select action a using ε-greedy policy
    • Take action, observe reward r and next state s'
    • Update Q-value: 
      Q(s, a) \leftarrow Q(s, a) + \alpha [r + \gamma \max_{a'} Q(s', a') - Q(s, a)]
    • Repeat until terminal state

Implementation Steps

  • Define environment states and actions
  • Initialize Q-table dimensions
  • Set hyperparameters: learning rate (α), discount factor (γ), exploration rate (ε)
  • Train agent through episodes
Q_Learning_Structure

For deeper understanding, explore our Reinforcement Learning Basics guide. This tutorial demonstrates how to implement Q-Learning in Python using OpenAI Gym.

Q_Learning_Process

Want to see code examples? Check out Q-Learning Implementation for step-by-step Python code.