Okay, I need to create a Wikipedia-style document about Q-learning under the categories of Technology > Artificial Intelligence > Reinforcement Learning. Let me start by understanding what Q-learning is. From what I remember, it's a type of reinforcement learning algorithm where an agent learns to make decisions by interacting with an environment. The key concept is the Q-value, which represents the value of taking a specific action in a specific state.

First, the structure: H1 for the title, then an overview section. Next, sections on principles, algorithms, applications, advantages/disadvantages, and maybe some references. I should make sure each section has appropriate subheadings (H2, H3).

For the introduction, I need to define Q-learning clearly, mention its purpose in reinforcement learning, and perhaps a brief history. Then, explain the core concepts like states, actions, rewards, and Q-values. Maybe include the Bellman equation here since it's fundamental.

In the algorithm section, I should outline the steps of how Q-learning works. Mention the update rule using the formula: Q(s,a) = Q(s,a) + α [R(s,a) + γ max Q(s',a') - Q(s,a)]. Also, explain terms like learning rate (α), discount factor (γ), and exploration vs exploitation.

Applications are important to show real-world use cases. Maybe include examples like robotics, game playing (like AlphaGo?), or recommendation systems. But I need to verify if those are accurate. Also, mention challenges like the curse of dimensionality or computational complexity.

Advantages and disadvantages: Q-learning is model-free, which is a plus because it doesn't require knowing the environment's dynamics. It can handle stochastic environments. However, it might struggle with large state spaces, leading to the need for function approximation methods like deep Q-networks (DQN). Also, convergence issues if not properly tuned.

I should check if I'm using terms correctly. For example, is Q-learning a model-based or model-free method? It's model-free because it doesn't require a model of the environment. That's an important point to clarify.

Need to make sure all technical terms are explained. For instance, "exploration vs exploitation" – explain that exploration is trying new actions to discover better rewards, while exploitation uses known actions for maximum reward. Maybe use a simple example here.

Also, include references or related topics at the end. Maybe link to other reinforcement learning methods like SARSA, Deep Q-Networks, or policy gradients. But since it's a wiki-style document, maybe just list them as related sections.

I need to avoid being too technical without explanation. For example, when discussing the Bellman equation, briefly explain what it represents in the context of Q-learning. Also, ensure that each section flows logically into the next.

Check for accuracy: The update rule is correct? Yes, the formula seems right. Learning rate (α) determines how much new information overrides old, discount factor (γ) determines future rewards' importance. Max Q(s',a') is the maximum value of the next state's actions.

Potential pitfalls to mention: If γ is too high, the agent might not learn effectively; if α is too large, it might overshoot optimal values. Also, the need for proper exploration strategies like ε-greedy.

In applications, maybe include specific examples like autonomous vehicles or game AI. But I should verify if Q-learning is actually used there or if DQN is more common now. Maybe mention that while Q-learning is foundational, modern approaches use neural networks to handle complex state spaces.

Make sure the document meets the 800-character minimum. Each section needs enough detail without being too verbose. Use bullet points for advantages and disadvantages, maybe a table comparing different RL methods if possible, but since it's about Q-learning specifically, perhaps not necessary unless there's space.

Check for clarity: Avoid jargon where possible, or explain it when used. For example, "agent" is the learner, "environment" is what the agent interacts with. Define these terms in the introduction.

Finally, ensure that all sections are properly formatted with H2 and H3 headings as needed. Use bold or italic for key terms if appropriate, but follow markdown syntax correctly.