arXiv Machine Learning
Aug 18
Temporal Logic Guided Universal Task Representations for Reinforcement Learning
arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.
Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
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.
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arXiv AI
Aug 18
Zero-Shot Instruction Following in RL via Structured LTL Representations
arXiv:2602. 14344v2 Announce Type: replace-cross Abstract: We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training.
arXiv AI
Aug 18
Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization
arXiv:2602. 09761v2 Announce Type: replace-cross Abstract: In this work we address the problem of training a Reinforcement Learning agent to follow multiple temporally-extended instructions expressed in Linear Temporal Logic in sub-symbolic environments.
arXiv AI
4d ago
Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
arXiv:2609.38065v1 Announce Type: cross Abstract: Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides...
arXiv AI
Jun 9
Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies
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).
arXiv AI
Aug 5
Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks
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 AI
Sep 11
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
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 AI
Jul 1
HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation
arXiv:2606. 30966v1 Announce Type: new Abstract: Formal specification is a powerful tool to guide the learning process and provides significant advantages over reward shaping: (1) mathematical rigor; (2) expressiveness to specify objectives and constraints, and (3) the ability to define tactics to achieve objectives.
arXiv AI
Aug 17
Reward Machines for Signal Temporal Logic
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 Machine Learning
Aug 17
hint$^2$: Hierarchical World Models for Inference-Time Temporal Logic Guidance
arXiv:2608. 13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime.
arXiv AI
Sep 12
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
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 Machine Learning
Jul 1
Prompting Robot Teams with Natural Language
arXiv:2509. 24575v2 Announce Type: replace-cross Abstract: This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions.