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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.
Model-Based Reinforcement Learning is a glossary concept in the paradigms layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Model-Based Reinforcement Learning |
| Category | Paradigms |
| Definition | 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. |
| Related Models | 4 |
| Related Research | 1 |
| 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 |
|---|---|---|
| Imagination-Based Learning | Training | A training paradigm where an agent uses its world model to generate synthetic (imagined) trajectories, then learns policies from these imagined experiences rather than costly real-world interaction. This dramatically improves sample efficiency. |
| Sample Efficiency | Concepts | The ability of an algorithm to learn effectively from a small number of real-world interactions. Model-based RL agents achieve high sample efficiency by supplementing real data with imagined experience from their world model. |
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
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