mlsanityData to 7 Oct 2026

On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies

arXiv 2403.16824 ↗First posted 25 Mar 2024cs.AIcited by 1

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Abstract (arXiv)

Recently, a simple but powerful language for expressing and learning general policies and problem decompositions (sketches) has been introduced in terms of rules defined over a set of Boolean and numerical features. In this work, we consider three extensions of this language aimed at making policies and sketches more flexible and reusable: internal memory states, as in finite state controllers; indexical features, whose values are a function of the state and a number of internal registers that can be loaded with objects; and modules that wrap up policies and sketches and allow them to call each other by passing parameters. In addition, unlike general policies that select state transitions rather than ground actions, the new language allows for the selection of such actions. The expressive power of the resulting language for policies and sketches is illustrated through a number of examples.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 41 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

15 of 41 references.

References · 15

  1. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 282
  2. 2020DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningcited by 159
  3. 2017A Laplacian Framework for Option Discovery in Reinforcement Learningcited by 108
  4. 2022Deep Hierarchical Planning from Pixelscited by 96
  5. 2019ASNets: Deep Learning for Generalised Planningcited by 42
  6. 2017Learning Generalized Reactive Policies using Deep Neural Networkscited by 39
  7. 2021Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limitscited by 38
  8. 2020Generalized Planning With Deep Reinforcement Learningcited by 35
  9. 2017Action Schema Networks: Generalised Policies with Deep Learningcited by 32
  10. 2018Features, Projections, and Representation Change for Generalized Planningcited by 25
  11. 2022Learning Generalized Policies Without Supervision Using GNNscited by 22
  12. 2021Generalized Planning as Heuristic Searchcited by 18
3 more
  1. 2019Generalized Planning With Procedural Domain Control Knowledgecited by 11
  2. 2023General Policies, Subgoal Structure, and Planning Widthcited by 6
  3. 2020Symbolic Network: Generalized Neural Policies for Relational MDPscited by 3

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