How does Q-learning work in reinforcement learning?

Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn the optimal actions to take in different states of an environment. It does this by iteratively updating a Q-table, which stores the expected long-term rewards (Q-values) for every possible state-action pair. The core idea is that the agent explores the environment, observes rewards, and adjusts its Q-values using the Bellman equation: Q(s,a) = Q(s,a) + α * [reward + γ * max(Q(s’,a’)) - Q(s,a)], where α is the learning rate, γ is the discount factor (prioritizing immediate vs. future rewards), and max(Q(s’,a’)) represents the best expected value from the next state (s’). Over time, this process converges to the optimal policy.

A key aspect of Q-learning is balancing exploration and exploitation. For example, in a grid-world game, the agent might start by choosing random moves (exploration) to discover rewards but gradually shift to taking actions with the highest Q-values (exploitation). Techniques like ε-greedy policies, where the agent randomly explores with probability ε (e.g., 10% of the time), help prevent getting stuck in suboptimal strategies. Imagine a robot navigating a maze: early on, it might try moving in all directions to map the environment, but later it prioritizes the shortest path to the goal based on learned Q-values.

In practice, Q-learning works well for environments with discrete states and actions, like board games or simple navigation tasks. However, it struggles with large or continuous state spaces (e.g., video games with pixel inputs) because the Q-table becomes impractical to store. This limitation led to the development of Deep Q-Networks (DQN), which replace the table with a neural network to approximate Q-values. For instance, DQN has been used to play Atari games by taking raw pixels as input and outputting Q-values for each possible joystick action. While Q-learning is foundational, extensions like Double Q-learning (to reduce overestimation of values) or prioritized experience replay (to prioritize important transitions) are often needed for complex scenarios.