What are the trade-offs between model-free and model-based Q-Learning?

Model-free Q-learning learns directly from experience, while model-based methods learn environment dynamics.

Model-free Q-learning is simpler but sample-inefficient. Model-based RL builds a transition model to simulate experiences, improving sample efficiency but introducing model bias. Hybrid approaches attempt to combine both for better performance.