Q-Learning is a model-free reinforcement learning algorithm that helps an agent learn the best action to take in a given state to maximize cumulative rewards.
It doesn’t require a model of the environment and uses trial-and-error learning to update its knowledge.
How Q-Learning Works
- Initialize Q-Table: Stores Q-values for each state-action pair.
- Choose Action: Select an action using a strategy like ε-greedy.
- Take Action: Execute the action in the environment.
- Receive Reward: Observe the outcome and reward.
- Update Q-Value: Apply the Q-Learning formula:
Q(s,a)=Q(s,a)+α[r+γmaxQ(s′,a′)−Q(s,a)]Q(s, a) = Q(s, a) + \alpha [r + \gamma \max Q(s’, a’) – Q(s, a)]Q(s,a)=Q(s,a)+α[r+γmaxQ(s′,a′)−Q(s,a)]
- Repeat: Iterate until the Q-values converge to optimal policy.
Advantages of Q-Learning
- Learns optimal policies without environment model
- Converges to the best action over time
- Works well for discrete state and action spaces
- Simple and widely used in reinforcement learning
Disadvantages
- Not ideal for large or continuous state spaces
- May require many iterations to converge
- Sensitive to learning rate and exploration strategy
Real-World Examples
- Robot navigation in unknown environments
- Game AI learning optimal moves
- Inventory management for warehouses
- Traffic signal control optimization
- Autonomous drones path planning
Conclusion
Q-Learning is a foundational reinforcement learning algorithm that enables agents to discover optimal strategies through experience and rewards.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/