> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Based Algorithms and World Models

> Reinforcement learning with learned environment models.

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Model-based reinforcement learning algorithms learn a model of the environment dynamics and use it for planning, often achieving better sample efficiency than model-free methods.

## Key Concepts

* **[World Models](/book/world-models/index)**: Neural networks that predict future states, see the full tutorial under Physical AI
* **Model Predictive Control (MPC)**: Planning with learned dynamics
* **Dyna Architecture**: Combining real and simulated experience
* **Latent Space Models**: Learning compressed state representations

**Key references**: (Schmidhuber, 2015; Lillicrap et al., 2015; Mnih et al., 2013)

## References

* Lillicrap, T., Hunt, J., Pritzel, A., Heess, N., Erez, T., et al. (2015). *Continuous control with deep reinforcement learning*.
* Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., et al. (2013). *Playing Atari with Deep Reinforcement Learning*.
* Schmidhuber, J. (2015). *On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models*.

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