1. What is the difference between Q learning and deep Q learning?

Q learning stores action-value estimates in a table. This works for small, finite environments. Deep Q learning replaces the table with a neural network, which makes it possible to handle complex, high-dimensional inputs like images or continuous state spaces. Both follow the same core logic but differ in how Q values are stored and updated.

2. Is Q learning supervised or unsupervised?

Q learning is neither. It belongs to reinforcement learning, which is a separate category. The agent does not learn from labelled examples. It learns by interacting with an environment and receiving rewards or penalties based on the actions it takes. The goal is to maximise cumulative reward over time.

3. What is the role of the discount factor (gamma) in Q learning?

Gamma controls how much the agent values future rewards compared to immediate ones. A gamma close to 1 means the agent thinks long-term and cares about rewards far in the future. A gamma close to 0 makes it focus only on immediate rewards. Choosing the right gamma depends on the nature of the task.

4. Can Q learning work in continuous action spaces?

Standard Q learning struggles with continuous action spaces because it requires discrete states and actions for the Q table. For continuous environments, variants like Deep Deterministic Policy Gradient (DDPG) or Soft Actor-Critic (SAC) are better suited. These extend the ideas of Q learning to handle continuous settings.

5. How long does Q learning take to converge?

Convergence time depends on the size of the environment, the learning rate, the discount factor, and the exploration strategy. Simple environments can converge in thousands of episodes. Complex environments may require millions. Poorly tuned hyperparameters can slow convergence significantly or prevent it altogether.

6. What is an episode in Q learning?

An episode is one complete run of the agent through the environment from start to a terminal state. For example, in a maze, one episode might be the agent starting at the entrance and either reaching the exit or hitting a dead end. The agent goes through many episodes during training to improve its Q values.

7. What happens if the reward function in Q learning is poorly designed?

If the reward function is poorly designed, the agent can learn the wrong behaviour. This is called reward hacking. The agent finds ways to maximise the reward signal in unintended ways. Designing a reward function that actually reflects the goal you want the agent to achieve is one of the most challenging parts of applying Q learning in practice.

8. How is Q learning used in natural language processing (NLP)?

Q learning is not the standard tool for NLP, but it has been used in dialogue systems and chatbots. The agent learns which response to give based on the conversation state and user feedback. It is also explored in tasks like text summarisation and machine translation where a sequence of decisions must be made.

9. What is the Q learning convergence guarantee?

Q learning is guaranteed to converge to the optimal Q values under specific conditions: the environment must be a finite Markov Decision Process, all state-action pairs must be visited infinitely often, and the learning rate must decay over time following certain mathematical conditions. In practice, these conditions are difficult to meet exactly.

10. How do you choose the learning rate in Q learning?

A high learning rate (close to 1) makes the agent update Q values quickly but can lead to instability. A low learning rate (close to 0) makes updates slow and the agent may take too long to learn. A common approach is to start with a moderate value like 0.1 and tune it based on how well the agent learns during training.

11. Is Q learning still relevant with newer reinforcement learning algorithms available?

Yes. Q learning remains a foundational algorithm that most advanced reinforcement learning methods build upon. Even if you end up using proximal policy optimisation or deep deterministic policy gradients in practice, understanding Q learning gives you the conceptual grounding to learn those methods faster. It is still taught and used as a baseline in research.