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aiguideReading Stanford CS234 Reinforcement Learning

Planning Plus Learning: MCTS, UCT, and AlphaGo/AlphaZero | Stanford CS234 Topic Guide

Until now, CS234 has computed one policy for the whole state space. Lectures 13 and 14 ask a different question: if I only care about the move in front of me, can extra local computation make that one decision better? The path runs from simple Monte Carlo search through the expectimax tree to MCTS, and treating each tree node as a bandit gives UCT. AlphaZero ties MCTS to a single network that predicts both policy and value, and self-play pushes both forward. The slides borrow figures from Silver et al. 2017 to answer three questions: how much architecture matters, how much MCTS adds, and whether human data is needed.

#cs234#ai-course#stanford#reinforcement-learning#mcts#alphazero
aideep-diveReading CMU 07-280 AI & ML I

How Monte Carlo Tree Search Connects to AlphaZero | CMU 07-280 Lecture 24

Spring 2026 Lecture 24 is MCTS, not Fall 2026 LLM post-training. It allocates simulations through selection, expansion, rollout, backup, and UCB, then connects policy/value heads and self-play to AlphaZero.

#cmu#ai-course#monte-carlo-tree-search#alphazero#reinforcement-learning
aideep-diveReading CMU 07-280 AI & ML I

Stage Review III: From MDPs and Q-learning to AlphaZero | CMU 07-280 Topic Guide

Stage III connects value, policy, bootstrapping, function approximation, and MCTS into AlphaZero: a network supplies priors and estimates, search improves decisions, and self-play creates the next training set.

#cmu#ai-course#reinforcement-learning#mcts#alphazero