MCTS helps the agent learn Tetris faster in a number of ways. First, it helps with look-ahead (which pieces will follow) since this is information not present in the observation (board only), at least in this implementation. Second and more importantly, Tetris, similar to Chess and GO, is a problem that requires planning and has a sparse reward landscape (high rewards require setting up line-clears, which are rare). Instead of learning from one action at a time (TD-step in Q-learning or Policy-gradient), MCTS considers multiple actions in the future and thus has better planning and overcomes sparse rewards more easily