Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn the optimal actions to take in an environment through trial and error. The core idea is to create a Q-table, which stores a value (Q-value) for each possible state-action pair. This Q-value represents the expected long-term reward of taking a specific action in a given state. The agent updates these values iteratively by interacting with the environment, balancing exploration (trying new actions) and exploitation (using known high-reward actions). Over time, the Q-table converges to reflect the best possible actions for each state.
The algorithm uses the Bellman equation to update Q-values. For example, consider a robot navigating a grid to reach a goal. When the robot moves from state s to s’ by taking action a, it receives a reward r. The Q-value for (s, a) is updated using the formula:
Q(s,a) = Q(s,a) + α * [r + γ * max(Q(s',a')) - Q(s,a)]
Here, α (learning rate) controls how much new information overrides old values, and γ (discount factor) determines the importance of future rewards. If the robot finds a path that yields a high reward, the Q-values along that path are reinforced. Exploration is often managed using strategies like ε-greedy, where the agent randomly explores with probability ε and exploits the best-known action otherwise.
While Q-learning is effective for small, discrete state spaces, it struggles with scalability. For instance, a video game with millions of possible states (e.g., pixel-based inputs) would require an impractically large Q-table. This limitation led to innovations like Deep Q-Networks (DQN), which replace the table with a neural network to approximate Q-values. However, Q-learning remains foundational for understanding reinforcement learning principles. Developers should note challenges like tuning hyperparameters (α, γ, ε) and ensuring sufficient exploration. In practical implementations, techniques like experience replay (storing past transitions) or decaying ε over time can improve stability and performance.