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Model-based reinforcement learning is an approach where agents learn a predictive model of the environment (a world model) and use it to simulate outcomes, plan ahead, and learn from imagined experience.
World models that learn environment dynamics for reinforcement learning through imagination and planning.
Static category overview generated from local editorial data.
| Attribute | Value |
|---|---|
| Category | Model-Based RL |
| Description | World models that learn environment dynamics for reinforcement learning through imagination and planning. |
| Definition | Model-based reinforcement learning is an approach where agents learn a predictive model of the environment (a world model) and use it to simulate outcomes, plan ahead, and learn from imagined experience. |
| Related Models | 10 |
| Related Research | 1 |
| Related Guides | 2 |
| Model | Lab | Category | Year |
|---|---|---|---|
| DreamerV3 | Google DeepMind | Model-Based RL | 2023 |
| DreamerV2 | Model-Based RL | 2021 | |
| PlaNet | Model-Based RL | 2019 | |
| MuZero | Google DeepMind | Model-Based RL | 2020 |
| Predictron | Google DeepMind | Model-Based RL | 2017 |
| TD-MPC2 | MIT / Meta | Model-Based RL | 2024 |
| World Models (Ha & Schmidhuber) | Google Brain / IDSIA | Model-Based RL | 2018 |
| IRIS | Microsoft Research | Model-Based RL | 2023 |
| Imagination-Augmented Agents (I2A) | Google DeepMind | Model-Based RL | 2017 |
| Value Prediction Network (VPN) | University of Michigan / Google Brain | Model-Based RL | 2017 |
| 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. |
| Guide | Summary |
|---|---|
| World Models for Beginners | A comprehensive introduction to AI world models: what they are, how they work, and why they matter for the future of robotics, reinforcement learning, and embodied AI. |
| Building World Models: A Practical Guide | A practical guide to implementing world models: from choosing architectures and training setups to debugging dynamics learning and policy optimization. |
FAQ answers rendered directly into static HTML for extractable responses.
Model-based RL is an approach where agents learn a predictive model of the environment and use it for imagination, planning, and sample-efficient learning.
DreamerV3 is a leading general system, MuZero is strong in planning-heavy discrete domains, and TD-MPC2 is prominent for continuous control and robotics.
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
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