The paper introduces Temporal-Logic-based Causal Diagrams (TL-CDs) for reinforcement learning tasks that involve temporally extended goals. TL-CDs encode causal relationships among environmental properties, complementing deterministic finite automata that model rewards. By leveraging TL-CDs, the authors design an RL algorithm that can predict expected rewards early, leading to significantly reduced exploration and faster convergence to optimal policies.
arXiv:2609.07461v1 Announce Type: new Abstract: We consider reinforcement learning in environments with dynamics that undergo an irreversible phase transition governed by a hidden temporal pattern. T...
arXiv:2608. 13625v1 Announce Type: new Abstract: Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring satisfaction.
arXiv:2607. 16421v1 Announce Type: new Abstract: It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning.
arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.
The paper introduces an end‑to‑end model‑based reinforcement learning algorithm that synthesises policies satisfying Linear Temporal Logic (LTL) specifications in unknown environments. It synchronises a Limit‑Deterministic Büchi Automaton (LDBA) with a Bayes‑Adaptive Markov Decision Process (BAMDP) and proposes a novel Bayes‑Adaptive Monte‑Carlo Planning (BAMCP) method for approximate Bayes‑optimal strategy synthesis. Experiments on finite and infinite‑horizon tasks show improved property satisfaction and sample efficiency compared to model‑free baselines, and ablation studies confirm the advantage of the new BAMCP over classical variants, including reduced task violations in cautious RL settings.
arXiv:2602. 06746v2 Announce Type: replace Abstract: We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks.
arXiv:2607. 14886v1 Announce Type: new Abstract: Temporal Knowledge Graph (TKG) reasoning under the extrapolation setting focuses on forecasting future time-stamped events (facts) from historical data in a temporal knowledge graph.
arXiv:2608. 03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents.
arXiv:2606. 08312v1 Announce Type: new Abstract: In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite traces (LTLf).
The paper reviews hierarchical reinforcement learning (HRL) as a method for enabling agents to explore, plan, and learn in complex, open-ended environments by uncovering temporal structure in experience streams. It discusses the unclear definition of what makes a structure useful, the benefits of HRL for decision‑making challenges, and its impact on AI agent performance trade‑offs. The authors survey various HRL methods—from online learning to offline datasets and large language model integration—and outline the challenges and suitable domains for temporal structure discovery.
arXiv:2508. 14751v2 Announce Type: replace Abstract: We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces.
arXiv:2606. 17680v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents.