Comp-LTL: Temporal Logic Planning via Zero-Shot Policy Composition
On the map: Deep reinforcement learning methods → Interpretable and logic-guided RL
Abstract (arXiv)
This work develops a zero-shot mechanism, Comp-LTL, for an agent to satisfy a Linear Temporal Logic (LTL) specification given existing task primitives trained via reinforcement learning (RL). Autonomous robots often need to satisfy spatial and temporal goals that are unknown until run time. Prior work focuses on learning policies for executing a task specified using LTL, but they incorporate the specification into the learning process. Any change to the specification requires retraining the policy, either via fine-tuning or from scratch. We present a more flexible approach -- to learn a set of composable task primitive policies that can be used to satisfy arbitrary LTL specifications without retraining or fine-tuning. Task primitives can be learned offline using RL and combined using Boolean composition at deployment. This work focuses on creating and pruning a transition system (TS) representation of the environment in order to solve for deterministic, non-ambiguous, and feasible solutions to LTL specifications given an environment and a set of task primitive policies. We show that our pruned TS is deterministic, contains no unrealizable transitions, and is sound. We verify our approach via simulation and compare it to other state of the art approaches, showing that Comp-LTL is safer and more adaptable.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 16 references that are in the map. First posted on arXiv 8 Aug 2024.
Builds on
5 of 16 references.
References · 5
- 2016Reinforcement Learning With Temporal Logic Rewardscited by 117
- 2020A Boolean Task Algebra for Reinforcement Learningcited by 48
- 2022From Spot 2.0 to Spot 2.10: What's New?cited by 22
- 2022Skill Machines: Temporal Logic Skill Composition in Reinforcement Learningcited by 9
- 2022Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specificationscited by 6
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- 2024DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL
- 2026Zero-Shot Instruction Following in RL via Structured LTL Representations
- 2025One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learning
- 2026PlatoLTL: Scaling LTL-Guided Multi-Task RL
- 2025Zero-Shot Instruction Following in RL via Structured LTL Representations
- 2025Automaton Constrained Q-Learning
- 2026hint2: Hierarchical World Models for Inference-Time Temporal Logic Guidance
- 2026Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions