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Aug 2026
A survey on LLM-enhanced reinforcement learning in financial markets
A three-paradigm taxonomy (feature-based, auxiliary-based, and policy-based) based on the functional role of LLMs within the RL pipeline is proposed, which provides superior scalability and stability, though often at the expense of representational depth.
Ghusoon Hadi al-Aldaffaie, Alireza Taheri, Amirfarhad Farhadi et al.
· Discover Artificial Intellig... · 0 citations
Conference
Open access
2026
Towards Automated Reinforcement Learning: Applications and Prospects of Large Language Models as Cognitive Components in the Full RL Lifecycle
: Deep reinforcement learning (DRL) has made significant progress in recent years. However, it still faces multiple bottlenecks in real-world applications. Agents suffer from low sample efficiency, and designing effective reward functions remains very difficult. Furthermore, traditional DRL lacks general cognitive abil...
Sheng-Hao Yuan
· Proceedings of the 4th Inter... · 0 citations
Conference
Aug 2026
Modern Trends in Trajectory Planning for Two-Tiered Learning Architectures
Two-tiered organization provides a compact survey lens for autonomous control (AC): an upper tier supports learning, planning, and high-level (HL) mission reasoning, while a lower tier executes feedback control, safety filtering, and platform-specific constraints. This compact survey reviews hierarchical architectures...
Junseo Min, Hoyeong Lee, Hyojun Ahn et al.
· International Conference on... · 0 citations
2026
Cyber Task Automation With Knowledge-Infused Reinforcement Learning and LLM-Guided Policies
As cyber threats continue to evolve, there is a need for Autonomous Cyber Defense (ACD) strategies capable of fast and context-aware responses. Reinforcement learning (RL) has shown promise in automating cyber defense by exploring and learning effective countermeasures. However, RL often struggles with sparse reward si...
Md. Shamim Towhid, Shahrear Iqbal, E. C. Pinto et al.
· IEEE Transactions on Network... · 0 citations
Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap
A structured review of three key roles that RL plays in empowering OR, serving as an end-to-end solution method or as a component integrated within heuristic and exact OR methods for combinatorial optimization problems, and facilitating extended reality analysis through integration with digital twin systems is presente...
Ya-Han Lu, Dong-Yang Xia, Nurşen Aydın et al.
· 0 citations
Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR
DATPO (Difficulty-Adaptive Sentence-entropy-guided Tree-structured Policy Optimization) integrates difficulty-adaptive tree search with a sibling-diversity advantage term, explicitly promoting semantic diversity to expand reasoning coverage during training.
Young Kyu Yu, Sanghwan Jang, Hwanjo Yu
· 1 citation