How does Q-Learning behave when action spaces are extremely large?

Q-Learning becomes inefficient in large action spaces due to exhaustive evaluation of Q-values per action.

Since Q-learning requires evaluating Q(s, a) for all actions to select argmax, large discrete action spaces significantly increase computation and exploration difficulty. The agent struggles to sufficiently explore all actions, leading to sparse updates and slow convergence. This is why policy-based methods or action embedding techniques are often preferred in such settings.