What is the effect of action space size on Q-Learning performance?

Large action spaces significantly increase learning complexity and slow convergence.

Q-learning evaluates all actions per state, so increasing action space size increases computational cost and exploration difficulty. In high-dimensional action spaces, learning becomes inefficient due to sparse updates and poor exploration coverage. Techniques like action embeddings or policy-based methods are often preferred.