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A planning algorithm that builds a search tree by randomly sampling future action sequences and evaluating their outcomes. MuZero combines MCTS with a learned world model to plan without knowing the game rules, achieving superhuman performance in Go, chess, and Atari.
MCTS (Monte Carlo Tree Search) is a glossary concept in the planning layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | MCTS (Monte Carlo Tree Search) |
| Category | Planning |
| Definition | A planning algorithm that builds a search tree by randomly sampling future action sequences and evaluating their outcomes. MuZero combines MCTS with a learned world model to plan without knowing the game rules, achieving superhuman performance in Go, chess, and Atari. |
| Related Models | 1 |
| Related Research | 1 |
| Model | Lab | Category |
|---|---|---|
| MuZero | Google DeepMind | Model-Based RL |
| Topic | Summary |
|---|---|
| Model-Based Reinforcement Learning | What model-based reinforcement learning is, how world models enable imagination-based planning, and why Dreamer, MuZero, PlaNet, and TD-MPC2 matter. |
| Term | Category | Definition |
|---|---|---|
| Model-Based Reinforcement Learning | Paradigms | An RL approach where the agent learns a predictive model of the environment (a world model) and uses it to simulate outcomes, plan ahead, and learn from imagined experience. Contrasted with model-free RL which learns directly from interaction. |
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
This glossary page publishes stable definitions linked to related models, research topics, and primary-source context.
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