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On Tackling Complex Tasks with Reward Machines and Signal Temporal Logics

arXiv 2604.14440 ↗First posted 9 Apr 2026cs.AIcited by 0

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Abstract (arXiv)

We propose a Reinforcement Learning (RL) based control design framework for handling complex tasks. The approach extends the concept of Reward Machines (RM) with Signal Temporal Logic (STL) formulas that can be used for event generation. The use of STL allows not only a more efficient representation of rewards for complex tasks but also guiding the training process to converge towards behaviors satisfying specified requirements. We also propose an implementation of the framework that leverages the STL online monitoring algorithms. We illustrate the framework with three case studies (minigrid, cart-pole and high-way environments) with non-trivial tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 9 Apr 2026.

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14 of 30 references.

References · 14

  1. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 320
  2. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 183
  3. 2016Reinforcement Learning With Temporal Logic Rewardscited by 114
  4. 2019Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guaranteescited by 77
  5. 2018Omega-Regular Objectives in Model-Free Reinforcement Learningcited by 74
  6. 2021LTL2Action: Generalizing LTL Instructions for Multi-Task RLcited by 72
  7. 2018Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specificationscited by 69
  8. 2020A Composable Specification Language for Reinforcement Learning Taskscited by 68
  9. 2016Q-Learning for Robust Satisfaction of Signal Temporal Logic Specificationscited by 65
  10. 2015Robust Online Monitoring of Signal Temporal Logiccited by 39
  11. 2020Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulascited by 36
  12. 2020Reward Machines for Cooperative Multi-Agent Reinforcement Learningcited by 35
2 more
  1. 2019Transfer of Temporal Logic Formulas in Reinforcement Learningcited by 31
  2. 2020Model-based Reinforcement Learning from Signal Temporal Logic Specificationscited by 18

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