Q-Learning is a fundamental model-free reinforcement learning algorithm used to learn optimal actions in an environment. It's widely applied in robotics, game theory, and autonomous systems. Let's break it down!

💡 Core Concepts

  • Q-Value: Represents the expected utility of taking an action in a specific state.
  • Exploration vs. Exploitation: Balances trying new actions (exploration) with using known best actions (exploitation).
  • Reward System: Agents learn by maximizing cumulative rewards over time.

🚀 Algorithm Steps

  1. Initialize a Q-table with all state-action pairs set to 0
  2. For each episode:
    • Observe current state s
    • Choose action a using ε-greedy policy
    • Take action, observe reward r and next state s'
    • Update Q-table: 
      Q(s, a) = Q(s, a) + α[r + γ*max(Q(s', a')) - Q(s, a)]
  3. Repeat until convergence

📈 Key Advantages

  • No need for environment models
  • Simple to implement
  • Handles large state spaces effectively

🧠 Applications

  • Game-playing agents (e.g., chess, Go)
  • Resource management systems
  • Autonomous navigation

For deeper exploration, check our Reinforcement Learning guide to understand how Q-Learning fits into broader RL frameworks.

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