🔍 Q-Learning is a model-free machine learning algorithm used to learn the optimal policy in reinforcement learning (RL) scenarios. It estimates the value of actions in specific states, known as the Q-value, to guide decision-making.

Key Features of Q-Learning

  • Bellman Equation: Uses a recursive formula to update Q-values based on rewards and future states.
  • Discrete State/Action Space: Works best with finite, manageable environments.
  • Exploration vs. Exploitation: Balances trying new actions (exploration) and using known ones (exploitation) via epsilon-greedy strategies.
  • Dynamic Updates: Iteratively improves Q-values through experience.

Applications

🎮 Game AI: Training agents to play games like chess or video games.
🚗 Autonomous Driving: Decision-making for path planning and obstacle avoidance.
🛒 Recommendation Systems: Optimizing user interactions in e-commerce platforms.

Advantages & Limitations

Advantages:

  • No need for a complete model of the environment.
  • Simple to implement for discrete problems.

Limitations:

  • Slow convergence in complex environments.
  • Struggles with large or continuous state/action spaces.

For deeper insights, check our guide on Reinforcement Learning Basics.

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