# Why AlphaGo didn't use Deep Q-Learning?

In the previous research, in 2015, Deep Q-Learning shows its great performance on single player Atari Games. But why do AlphaGo's researchers use CNN + MCTS instead of Deep Q-Learning? is that because Deep Q-Learning somehow is not suitable for Go?

$$Q$$-learning (and also its deep variant, and most of the other well-known reinforcement learning algorithms) are inherently learning approaches for single-agent environments. The entire problem setting that these algorithms are developed for (Markov decision processes, or MDPs) is always framed in terms of a single agent situated in some environment, where that agent can take actions that have some level of influence over the states that they lead to, and rewards may be observed.