Thank you very much for the clear explanation for MCTS Vs. vanilla policy gradient >In vanilla policy gradient, one plays the game to the end and then bumps th

Thank you very much for the clear explanation for MCTS Vs. vanilla policy gradient >In vanilla policy gradient, one plays the game to the end and then bumps the probability of all actions taken by the agent up (if AlphaGo won) or down (if it lost). This is very slow because there ~150 moves in an expert game, and we do not know which moves caused decisive victory or loss - i.e. the problem of "long term credit assignment". >I think about MCTS in the following way: suppose you have a perfect "simulator" for some reinforcement learning task you are trying to accomplish (i.e. real-world robot grasping for a cup). Then instead of trying to grasp the cup over and over again, you can just try/"plan" in simulation until you arrive at a motion plan that picks up the cup.