Symmetries and Expressive Requirements for Learning General Policies
Fuente:
arXiv
Saved in:
| Main Authors: | Drexler, Dominik, Ståhlberg, Simon, Bonet, Blai, Geffner, Hector |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies
by: Bonet, Blai, et al.
Published: (2024)
by: Bonet, Blai, et al.
Published: (2024)
Learning More Expressive General Policies for Classical Planning Domains
by: Ståhlberg, Simon, et al.
Published: (2024)
by: Ståhlberg, Simon, et al.
Published: (2024)
Learning General Policies with Policy Gradient Methods
by: Ståhlberg, Simon, et al.
Published: (2025)
by: Ståhlberg, Simon, et al.
Published: (2025)
Learning General Policies From Examples
by: Bonet, Blai, et al.
Published: (2025)
by: Bonet, Blai, et al.
Published: (2025)
First-Order Representation Languages for Goal-Conditioned RL
by: Ståhlberg, Simon, et al.
Published: (2025)
by: Ståhlberg, Simon, et al.
Published: (2025)
Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
by: Aichmüller, Michael, et al.
Published: (2026)
by: Aichmüller, Michael, et al.
Published: (2026)
Learning to Ground Existentially Quantified Goals
by: Funkquist, Martin, et al.
Published: (2024)
by: Funkquist, Martin, et al.
Published: (2024)
Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains
by: Hofmann, Till, et al.
Published: (2024)
by: Hofmann, Till, et al.
Published: (2024)
Lifted Successor Generation in Numeric Planning
by: Drexler, Dominik
Published: (2025)
by: Drexler, Dominik
Published: (2025)
Learning to Search and Searching to Learn for Generalization in Planning
by: Aichmüller, Michael, et al.
Published: (2026)
by: Aichmüller, Michael, et al.
Published: (2026)
Learning Lifted STRIPS Models from Action Traces Alone: A Simple, General, and Scalable Solution
by: Gösgens, Jonas, et al.
Published: (2024)
by: Gösgens, Jonas, et al.
Published: (2024)
Towards Principled Graph Transformers
by: Müller, Luis, et al.
Published: (2024)
by: Müller, Luis, et al.
Published: (2024)
Learning Lifted Action Models From Traces of Incomplete Actions and States
by: Jansen, Niklas, et al.
Published: (2025)
by: Jansen, Niklas, et al.
Published: (2025)
Learning Lifted Action Models from Traces with Minimal Information About Actions and States
by: Gösgens, Jonas, et al.
Published: (2026)
by: Gösgens, Jonas, et al.
Published: (2026)
Sketch Decompositions for Classical Planning via Deep Reinforcement Learning
by: Aichmüller, Michael, et al.
Published: (2024)
by: Aichmüller, Michael, et al.
Published: (2024)
LLM-Evolved Pattern Generators for Optimal Classical Planning
by: Phung, Windy, et al.
Published: (2026)
by: Phung, Windy, et al.
Published: (2026)
Differentiable Learning of Lifted Action Schemas for Classical Planning
by: Reiter, Jonas, et al.
Published: (2026)
by: Reiter, Jonas, et al.
Published: (2026)
Parallel Lifted Planning via Semi-Naive Datalog Evaluation
by: Drexler, Dominik, et al.
Published: (2026)
by: Drexler, Dominik, et al.
Published: (2026)
Dynamic Tree Databases in Automated Planning
by: Joergensen, Oliver, et al.
Published: (2025)
by: Joergensen, Oliver, et al.
Published: (2025)
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
by: Schnaus, Dominik, et al.
Published: (2023)
by: Schnaus, Dominik, et al.
Published: (2023)
EXPO: Stable Reinforcement Learning with Expressive Policies
by: Dong, Perry, et al.
Published: (2025)
by: Dong, Perry, et al.
Published: (2025)
Remove Symmetries to Control Model Expressivity and Improve Optimization
by: Ziyin, Liu, et al.
Published: (2024)
by: Ziyin, Liu, et al.
Published: (2024)
IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements
by: Stol, Maarten C., et al.
Published: (2024)
by: Stol, Maarten C., et al.
Published: (2024)
Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
by: Doo, JaeHyeok, et al.
Published: (2026)
by: Doo, JaeHyeok, et al.
Published: (2026)
From Next Token Prediction to (STRIPS) World Models
by: Núñez-Molina, Carlos, et al.
Published: (2025)
by: Núñez-Molina, Carlos, et al.
Published: (2025)
Symmetry Considerations for Learning Task Symmetric Robot Policies
by: Mittal, Mayank, et al.
Published: (2024)
by: Mittal, Mayank, et al.
Published: (2024)
EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning
by: Huang, Peide, et al.
Published: (2024)
by: Huang, Peide, et al.
Published: (2024)
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing
by: Zhang, Hao, et al.
Published: (2025)
by: Zhang, Hao, et al.
Published: (2025)
Efficiently Generating Expressive Quadruped Behaviors via Language-Guided Preference Learning
by: Clark, Jaden, et al.
Published: (2025)
by: Clark, Jaden, et al.
Published: (2025)
Level Generation with Constrained Expressive Range
by: Bazzaz, Mahsa, et al.
Published: (2025)
by: Bazzaz, Mahsa, et al.
Published: (2025)
Understanding Expressivity of GNN in Rule Learning
by: Qiu, Haiquan, et al.
Published: (2023)
by: Qiu, Haiquan, et al.
Published: (2023)
Are Expressive Encoders Necessary for Discrete Graph Generation?
by: Revolinsky, Jay, et al.
Published: (2026)
by: Revolinsky, Jay, et al.
Published: (2026)
Expressive and Scalable Quantum Fusion for Multimodal Learning
by: Nguyen, Tuyen, et al.
Published: (2025)
by: Nguyen, Tuyen, et al.
Published: (2025)
Expressive Music Data Processing and Generation
by: Liu, Jingwei
Published: (2025)
by: Liu, Jingwei
Published: (2025)
Bounded Fitting for Expressive Description Logics
by: Funk, Maurice, et al.
Published: (2026)
by: Funk, Maurice, et al.
Published: (2026)
Building Expressive and Tractable Probabilistic Generative Models: A Review
by: Sidheekh, Sahil, et al.
Published: (2024)
by: Sidheekh, Sahil, et al.
Published: (2024)
Learning Infinitesimal Generators of Continuous Symmetries from Data
by: Ko, Gyeonghoon, et al.
Published: (2024)
by: Ko, Gyeonghoon, et al.
Published: (2024)
Expressive MIDI-format Piano Performance Generation
by: Liu, Jingwei
Published: (2024)
by: Liu, Jingwei
Published: (2024)
DeepJIVE: Learning Joint and Individual Variation Explained from Multimodal Data Using Deep Learning
by: Drexler, Matthew, et al.
Published: (2025)
by: Drexler, Matthew, et al.
Published: (2025)
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
by: Panangaden, Prakash, et al.
Published: (2023)
by: Panangaden, Prakash, et al.
Published: (2023)
Similar Items
-
On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies
by: Bonet, Blai, et al.
Published: (2024) -
Learning More Expressive General Policies for Classical Planning Domains
by: Ståhlberg, Simon, et al.
Published: (2024) -
Learning General Policies with Policy Gradient Methods
by: Ståhlberg, Simon, et al.
Published: (2025) -
Learning General Policies From Examples
by: Bonet, Blai, et al.
Published: (2025) -
First-Order Representation Languages for Goal-Conditioned RL
by: Ståhlberg, Simon, et al.
Published: (2025)