Monte Carlo Tree Search

撰写于 2018-09-07 修改于 2018-09-07 分类 Reinforcement Learning 标签 Reinforcement Learning

Monte Carlo Tree Search(MCTS) is a powerful method to generate optimal policies for AI. There are four steps in MCTS

  1. Seletion: select a best action in current state using UCB
  2. Expension: expand tree node
  3. Simulation: roll out to estimate the value of current state
  4. Update: update(backpropagate) parameters

Some helpful videos:

目录

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