Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Shek, Chak Lam, Tokekar, Pratap |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
When to Localize? A Risk-Constrained Reinforcement Learning Approach
by: Shek, Chak Lam, et al.
Published: (2024)
by: Shek, Chak Lam, et al.
Published: (2024)
Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach
by: Shek, Chak Lam, et al.
Published: (2025)
by: Shek, Chak Lam, et al.
Published: (2025)
Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
by: Liu, Rui, et al.
Published: (2026)
by: Liu, Rui, et al.
Published: (2026)
On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
by: Barakat, Anas, et al.
Published: (2024)
by: Barakat, Anas, et al.
Published: (2024)
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
by: McClellan, Joshua, et al.
Published: (2024)
by: McClellan, Joshua, et al.
Published: (2024)
Adaptive Conformal Guidance for Learning under Uncertainty
by: Liu, Rui, et al.
Published: (2025)
by: Liu, Rui, et al.
Published: (2025)
CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems
by: Liu, Rui, et al.
Published: (2025)
by: Liu, Rui, et al.
Published: (2025)
Decision-Oriented Learning Using Differentiable Submodular Maximization for Multi-Robot Coordination
by: Shi, Guangyao, et al.
Published: (2023)
by: Shi, Guangyao, et al.
Published: (2023)
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm
by: Shek, Chak Lam, et al.
Published: (2025)
by: Shek, Chak Lam, et al.
Published: (2025)
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL
by: McClellan, Joshua, et al.
Published: (2025)
by: McClellan, Joshua, et al.
Published: (2025)
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
by: Liu, Rui, et al.
Published: (2024)
by: Liu, Rui, et al.
Published: (2024)
VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferences
by: Singh, Anukriti, et al.
Published: (2025)
by: Singh, Anukriti, et al.
Published: (2025)
Hierarchical Meta-Reinforcement Learning via Automated Macro-Action Discovery
by: Cho, Minjae, et al.
Published: (2024)
by: Cho, Minjae, et al.
Published: (2024)
Joint Learning of Hierarchical Neural Options and Abstract World Model
by: Piriyakulkij, Wasu Top, et al.
Published: (2026)
by: Piriyakulkij, Wasu Top, et al.
Published: (2026)
Cog-Rethinker: Hierarchical Metacognitive Reinforcement Learning for LLM Reasoning
by: Sun, Zexu, et al.
Published: (2025)
by: Sun, Zexu, et al.
Published: (2025)
Boosting deep Reinforcement Learning using pretraining with Logical Options
by: Ye, Zihan, et al.
Published: (2026)
by: Ye, Zihan, et al.
Published: (2026)
Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents
by: Zhen, Shuai, et al.
Published: (2026)
by: Zhen, Shuai, et al.
Published: (2026)
OptionZero: Planning with Learned Options
by: Huang, Po-Wei, et al.
Published: (2025)
by: Huang, Po-Wei, et al.
Published: (2025)
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning
by: Feng, Xinsong, et al.
Published: (2025)
by: Feng, Xinsong, et al.
Published: (2025)
Think like a Scientist: Physics-guided LLM Agent for Equation Discovery
by: Yang, Jianke, et al.
Published: (2026)
by: Yang, Jianke, et al.
Published: (2026)
Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
by: Lee, Joongkyu, et al.
Published: (2025)
by: Lee, Joongkyu, et al.
Published: (2025)
Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning
by: Ahn, Hongjoon, et al.
Published: (2025)
by: Ahn, Hongjoon, et al.
Published: (2025)
SOAP-RL: Sequential Option Advantage Propagation for Reinforcement Learning in POMDP Environments
by: Ishida, Shu, et al.
Published: (2024)
by: Ishida, Shu, et al.
Published: (2024)
STeCa: Step-level Trajectory Calibration for LLM Agent Learning
by: Wang, Hanlin, et al.
Published: (2025)
by: Wang, Hanlin, et al.
Published: (2025)
Semantic-guided Representation Learning for Multi-Label Recognition
by: Zhang, Ruhui, et al.
Published: (2025)
by: Zhang, Ruhui, et al.
Published: (2025)
LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning
by: Yao, Chang, et al.
Published: (2026)
by: Yao, Chang, et al.
Published: (2026)
Taxon: Hierarchical Tax Code Prediction with Semantically Aligned LLM Expert Guidance
by: Li, Jihang, et al.
Published: (2026)
by: Li, Jihang, et al.
Published: (2026)
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
by: Nair, Lakshmi, et al.
Published: (2025)
by: Nair, Lakshmi, et al.
Published: (2025)
Goal Discovery with Causal Capacity for Efficient Reinforcement Learning
by: Yu, Yan, et al.
Published: (2025)
by: Yu, Yan, et al.
Published: (2025)
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning
by: Hao, Ce, et al.
Published: (2023)
by: Hao, Ce, et al.
Published: (2023)
DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
by: Sharma, Shashank, et al.
Published: (2025)
by: Sharma, Shashank, et al.
Published: (2025)
Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning
by: Cipollone, Roberto, et al.
Published: (2025)
by: Cipollone, Roberto, et al.
Published: (2025)
Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback
by: Kim, Gihoon, et al.
Published: (2026)
by: Kim, Gihoon, et al.
Published: (2026)
Deep Reinforcement Learning from Hierarchical Preference Design
by: Bukharin, Alexander, et al.
Published: (2023)
by: Bukharin, Alexander, et al.
Published: (2023)
HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
by: Lehna, Malte, et al.
Published: (2024)
by: Lehna, Malte, et al.
Published: (2024)
MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
by: Li, Dong, et al.
Published: (2026)
by: Li, Dong, et al.
Published: (2026)
Boosting Hierarchical Reinforcement Learning with Meta-Learning for Complex Task Adaptation
by: Khajooeinejad, Arash, et al.
Published: (2024)
by: Khajooeinejad, Arash, et al.
Published: (2024)
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning
by: Bhaskar, Amisha, et al.
Published: (2024)
by: Bhaskar, Amisha, et al.
Published: (2024)
Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm
by: Li, Qinru, et al.
Published: (2023)
by: Li, Qinru, et al.
Published: (2023)
Scalable Option Learning in High-Throughput Environments
by: Henaff, Mikael, et al.
Published: (2025)
by: Henaff, Mikael, et al.
Published: (2025)
Similar Items
-
When to Localize? A Risk-Constrained Reinforcement Learning Approach
by: Shek, Chak Lam, et al.
Published: (2024) -
Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach
by: Shek, Chak Lam, et al.
Published: (2025) -
Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
by: Liu, Rui, et al.
Published: (2026) -
On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
by: Barakat, Anas, et al.
Published: (2024) -
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
by: McClellan, Joshua, et al.
Published: (2024)