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Begriffe
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Transition Model

Neural component that predicts the next latent state given the current state and action, enabling the simulation of future imaginations.

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Variational Autoencoder

Neural network that encodes observations into probabilistic distribution in latent space and decodes them, enabling the generation of plausible states.

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Dreamer

World Model algorithm that learns entirely in latent space using imaginations to optimize the policy without real interaction.

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Imagination Rollout

Process of simulating future trajectories in latent space using the transition model to evaluate actions before execution.

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State Representation

Compressed and informative encoding of raw observation capturing relevant features for decision-making.

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Latent Planning

Decision process optimized in compressed latent space rather than raw observation space for computational efficiency.

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Model Predictive Control

Planning strategy that sequentially optimizes actions by simulating multiple future steps through the learned model.

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Curious Agent

World Model agent that intrinsically maximizes information or prediction error to efficiently explore the environment.

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Augmented Imagination

Technique for augmenting training data with imagined trajectories to improve robustness and learning efficiency.

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Latent Action Space

Compressed representation of actions in latent space, enabling more efficient planning and task transfer.

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Hierarchical Model

Multi-scale World Model architecture capturing both fast and slow dynamics for planning at different time horizons.

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