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kategorie
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podkategorie
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pojęcia
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pojęcia

Multi-level Policy Optimization

Coordinated optimization process adjusting parameters of multiple hierarchical policy layers using synchronized gradients to maximize overall reward.

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Nested Policy Networks

Neural network architecture where low-level policies are nested within high-level policies, enabling hierarchical decomposition of decisions and actions.

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Option Framework

Mathematical formalization of extended temporal behaviors in hierarchies, where each option combines an intra-option policy, initiation condition, and termination condition.

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Sub-policy Selection

Mechanism by which the high-level policy dynamically selects which sub-policy to activate based on the current state and objectives to be achieved.

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Primitive Actions

Fundamental lowest-level actions executed directly in the environment, constituting the building blocks of complex behaviors constructed by the hierarchy.

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Hierarchical Advantage Estimation

Advantage estimation technique accounting for the hierarchical structure, evaluating each level's contribution to overall performance improvement.

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Cross-level Gradient Flow

Gradient propagation mechanism across different hierarchical levels, ensuring coordinated and stable optimization of the entire architecture.

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Hierarchical Entropy Regularization

Regularization technique applying differentiated entropy penalties according to hierarchical levels to balance exploration and exploitation at each scale.

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Multi-timescale Learning

Learning paradigm where different hierarchical levels operate at distinct temporal scales, enabling efficient management of short and long-term decisions.

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Hierarchical Value Functions

Hierarchically structured value functions estimating expected returns at different levels of temporal abstraction to guide policy learning.

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