Temporal Logic Guided Universal Task Representations for Reinforcement Learning
Task guided agents demonstrate strong performance in a wide range of complex tasks. However, most existing task representation algorithms are tailored to specific contexts and struggle to generalize across diverse scenarios. Moreover, they typically depend on gradient signals from reinforcement learning controllers to update their weights, which can degrade both representation quality and learning efficiency. To overcome these limitations, we propose LOTUS, a temporal logic inspired universal task representation framework that can be seamlessly integrated into any RL algorithm to enhance agent performance across diverse task settings. Specifically, we design a novel task representation ar...
| Authors | Hao Zhang, Zhangli Zhou, Zhen Kan |
|---|---|
| Primary category | cs.RO |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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