🔍 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.