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| Attribute | Value |
|---|---|
| Model | World Models (Ha & Schmidhuber) |
| Lab / Organization | DeepMind |
| Category | Model-Based RL |
| Subtype | VAE + MDN-RNN |
| World Model Type | Generative latent dynamics model |
| Primary Domain | Game environments |
| Architecture | VAE (visual encoder) + MDN-RNN (dynamics) + linear controller |
| Modality | Visual |
| Training Method | Unsupervised VAE training + RNN dynamics learning + evolutionary policy search |
| Status | foundational |
| Year | 2018 |
| Performance Index | 48/100 (high confidence, v1.1) |
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Ha and Schmidhuber's World Models paper demonstrated that an agent can learn a compressed spatial and temporal representation of the environment, then train a policy entirely within this learned 'dream' world. The architecture uses a VAE for spatial encoding and an MDN-RNN for temporal dynamics, creating a generative model of the environment that the agent can imagine in.
World Models (Ha & Schmidhuber) is a generative latent dynamics model developed by Google Brain / IDSIA in 2018 for game environments.
Short extractable facts for answer engines and no-JS readers.
| Signal | Value |
|---|---|
| Definition | World Models (Ha & Schmidhuber) is a generative latent dynamics model developed by Google Brain / IDSIA in 2018 for game environments. |
| Short Description | The seminal paper that popularized the concept of world models: learning to imagine environments and training policies entirely in dreams. |
| Benchmark Rows | 1 |
| FAQ Entries | 1 |
| Related Models | 3 |
| Related Guides | 3 |
| Related Research Topics | 3 |
| Last Updated | 2026-02-10 |
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Published benchmark evidence attached to this model record.
| Benchmark | Metric | Result | Source |
|---|---|---|---|
| CarRacing-v0 | Avg Score 906 / 1000 | Competitive (dream-trained) | Source |
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| Reference | Link |
|---|---|
| Ha, D. & Schmidhuber, J., 2018. World Models. NeurIPS 2018. | Open source |
Side-by-side comparisons already connected to this model.
| Comparison | Matchup | Summary |
|---|---|---|
| Ha & Schmidhuber World Model vs DreamerV3 | Ha & Schmidhuber World Model vs DreamerV3 | The original 2018 'World Models' paper vs. the current state-of-the-art: how five years of research transformed a foundational concept into a domain-general world model agent. |
| AMI vs Ha World Model | AMI vs Ha World Model | Two pioneering cognitive-inspired world models: Ha's 2018 World Model introduced the VAE+RNN+Controller architecture, while AMI proposes an autonomous machine intelligence framework inspired by biological cognition. |
Crawler-readable guide links tied to this model.
| 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. |
| How to Read World Models Papers | A practical reading path through world-model research, from foundational concepts to latent dynamics, planning, simulators, and self-supervised approaches. |
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| 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. |
| Self-Supervised World Models | How self-supervised world models learn environment dynamics without rewards, from JEPA and V-JEPA to predictive latent representations. |
| World Models: A Comprehensive Survey | A survey of AI world models covering taxonomy, leading architectures, landmark systems, open challenges, and future research directions. |
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The concept existed earlier (e.g., Schmidhuber 1990), but Ha & Schmidhuber's 2018 paper popularized the term in the modern deep RL context and inspired the current wave of world model research.
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Published by world-models.io editorial board.
Lead editor Tyler D. - Technical editor, methodology and benchmark analysis.
This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.
Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-02-10.
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