mlsanityData to 7 Oct 2026

Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specifications

arXiv 2108.01317 ↗First posted 3 Aug 2021eess.SY cs.LG cs.SYcited by 1

Not on the map. Why?

Abstract (arXiv)

We apply deep reinforcement learning (DRL) to design of a networked controller with network delays to complete a temporal control task that is described by a signal temporal logic (STL) formula. STL is useful to deal with a specification with a bounded time interval for a dynamical system. In general, an agent needs not only the current system state but also the past behavior of the system to determine a desired control action for satisfying the given STL formula. Additionally, we need to consider the effect of network delays for data transmissions. Thus, we propose an extended Markov decision process using past system states and control actions, which is called a τ d-MDP, so that the agent can evaluate the satisfaction of the STL formula considering the network delays. Thereafter, we apply a DRL algorithm to design a networked controller using the τ d-MDP. Through simulations, we also demonstrate the learning performance of the proposed algorithm.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 3 Aug 2021.

Builds on

14 of 25 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2015Trust Region Policy Optimizationcited by 2,503
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  6. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  7. 2016Reinforcement Learning With Temporal Logic Rewardscited by 114
  8. 2016Q-Learning for Robust Satisfaction of Signal Temporal Logic Specificationscited by 65
  9. 2020Tractable Reinforcement Learning of Signal Temporal Logic Objectivescited by 21
  10. 2017A Policy Search Method For Temporal Logic Specified Reinforcement Learning Taskscited by 19
  11. 2020Model-based Reinforcement Learning from Signal Temporal Logic Specificationscited by 18
  12. 2018Deep Reinforcement Learning for Event-Triggered Controlcited by 16
2 more
  1. 2014On-Line Monitoring for Temporal Logic Robustnesscited by 15
  2. 2018DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Schedulingcited by 8

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxationcited by 8