Saved in:
| Main Authors: | Aichmüller, Michael, Hesse, Yannik, Geffner, Hector |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.25720 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Sketch Decompositions for Classical Planning via Deep Reinforcement Learning
by: Aichmüller, Michael, et al.
Published: (2024)
by: Aichmüller, Michael, et al.
Published: (2024)
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 Generalized Policies for Fully Observable Non-Deterministic Planning Domains
by: Hofmann, Till, et al.
Published: (2024)
by: Hofmann, Till, 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 From Examples
by: Bonet, Blai, et al.
Published: (2025)
by: Bonet, Blai, et al.
Published: (2025)
Differentiable Learning of Lifted Action Schemas for Classical Planning
by: Reiter, Jonas, et al.
Published: (2026)
by: Reiter, Jonas, et al.
Published: (2026)
Learning General Policies with Policy Gradient Methods
by: Ståhlberg, Simon, et al.
Published: (2025)
by: Ståhlberg, Simon, et al.
Published: (2025)
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)
Symmetries and Expressive Requirements for Learning General Policies
by: Drexler, Dominik, et al.
Published: (2024)
by: Drexler, Dominik, et al.
Published: (2024)
Learning to Ground Existentially Quantified Goals
by: Funkquist, Martin, et al.
Published: (2024)
by: Funkquist, Martin, 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)
First-Order Representation Languages for Goal-Conditioned RL
by: Ståhlberg, Simon, et al.
Published: (2025)
by: Ståhlberg, Simon, et al.
Published: (2025)
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)
Plan Before Search: Search Agents Need Plan
by: Qian, Zhipeng, et al.
Published: (2026)
by: Qian, Zhipeng, et al.
Published: (2026)
Hybrid Reinforcement Learning and Search for Flight Trajectory Planning
by: Luise, Alberto, et al.
Published: (2025)
by: Luise, Alberto, et al.
Published: (2025)
Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
by: Oswald, James, et al.
Published: (2026)
by: Oswald, James, et al.
Published: (2026)
Plans for Evaluating Structured Generative Search Summaries
by: Sakai, Tetsuya, et al.
Published: (2026)
by: Sakai, Tetsuya, et al.
Published: (2026)
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
by: Chen, Mingyang, et al.
Published: (2025)
by: Chen, Mingyang, et al.
Published: (2025)
Subgoal-Guided Policy Heuristic Search with Learned Subgoals
by: Tuero, Jake, et al.
Published: (2025)
by: Tuero, Jake, et al.
Published: (2025)
From Next Token Prediction to (STRIPS) World Models
by: Núñez-Molina, Carlos, et al.
Published: (2025)
by: Núñez-Molina, Carlos, et al.
Published: (2025)
Thought of Search: Planning with Language Models Through The Lens of Efficiency
by: Katz, Michael, et al.
Published: (2024)
by: Katz, Michael, et al.
Published: (2024)
Stream of Search (SoS): Learning to Search in Language
by: Gandhi, Kanishk, et al.
Published: (2024)
by: Gandhi, Kanishk, et al.
Published: (2024)
Learning to Reason via Program Generation, Emulation, and Search
by: Weir, Nathaniel, et al.
Published: (2024)
by: Weir, Nathaniel, et al.
Published: (2024)
Efficient Solution and Learning of Robust Factored MDPs
by: Schnitzer, Yannik, et al.
Published: (2025)
by: Schnitzer, Yannik, et al.
Published: (2025)
Iterated Local Search with Linkage Learning
by: Tinós, Renato, et al.
Published: (2024)
by: Tinós, Renato, et al.
Published: (2024)
Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
by: Bapoo, Ubayd, et al.
Published: (2025)
by: Bapoo, Ubayd, et al.
Published: (2025)
Heuristic Search for Multi-Objective Probabilistic Planning
by: Chen, Dillon, et al.
Published: (2023)
by: Chen, Dillon, et al.
Published: (2023)
AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
by: Sun, Jingbo, et al.
Published: (2026)
by: Sun, Jingbo, et al.
Published: (2026)
Multi-Objective Neural Architecture Search by Learning Search Space Partitions
by: Zhao, Yiyang, et al.
Published: (2024)
by: Zhao, Yiyang, et al.
Published: (2024)
Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search
by: Chen, Mengxiang, et al.
Published: (2026)
by: Chen, Mengxiang, et al.
Published: (2026)
Generative Active Learning for the Search of Small-molecule Protein Binders
by: Korablyov, Maksym, et al.
Published: (2024)
by: Korablyov, Maksym, et al.
Published: (2024)
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
by: Lehnert, Lucas, et al.
Published: (2024)
by: Lehnert, Lucas, et al.
Published: (2024)
Enhancing Reinforcement Learning Through Guided Search
by: Arjonilla, Jérôme, et al.
Published: (2024)
by: Arjonilla, Jérôme, et al.
Published: (2024)
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench
by: Toledo, Edan, et al.
Published: (2025)
by: Toledo, Edan, et al.
Published: (2025)
DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling
by: Sun, Hao, et al.
Published: (2025)
by: Sun, Hao, et al.
Published: (2025)
Online Learning of HTN Methods for integrated LLM-HTN Planning
by: Xu, Yuesheng, et al.
Published: (2025)
by: Xu, Yuesheng, et al.
Published: (2025)
Planning In Natural Language Improves LLM Search For Code Generation
by: Wang, Evan, et al.
Published: (2024)
by: Wang, Evan, et al.
Published: (2024)
Transformers Struggle to Learn to Search
by: Saparov, Abulhair, et al.
Published: (2024)
by: Saparov, Abulhair, et al.
Published: (2024)
AI-SearchPlanner: Modular Agentic Search via Pareto-Optimal Multi-Objective Reinforcement Learning
by: Mei, Lang, et al.
Published: (2025)
by: Mei, Lang, et al.
Published: (2025)
Similar Items
-
Sketch Decompositions for Classical Planning via Deep Reinforcement Learning
by: Aichmüller, Michael, et al.
Published: (2024) -
Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
by: Aichmüller, Michael, et al.
Published: (2026) -
Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains
by: Hofmann, Till, et al.
Published: (2024) -
Learning More Expressive General Policies for Classical Planning Domains
by: Ståhlberg, Simon, et al.
Published: (2024) -
Learning General Policies From Examples
by: Bonet, Blai, et al.
Published: (2025)