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A property of a world model whose predictions of future states depend explicitly on the action taken by the agent. Action-conditioned models support interactive simulation and planning, distinguishing true world models from passive video generators.
Action Conditioning is a glossary concept in the architecture layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Action Conditioning |
| Category | Architecture |
| Definition | A property of a world model whose predictions of future states depend explicitly on the action taken by the agent. Action-conditioned models support interactive simulation and planning, distinguishing true world models from passive video generators. |
| 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 |
|---|---|---|
| Latent Dynamics | Architecture | The learned transition function within a latent space that predicts how hidden states evolve over time in response to actions. Latent dynamics models avoid computationally expensive pixel-level predictions by operating in compressed representation space. |
| 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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