mlsanityData to 7 Oct 2026

Model-Free Learning of Safe yet Effective Controllers

arXiv 2103.14600 ↗First posted 26 Mar 2021cs.RO cs.FL cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

We study the problem of learning safe control policies that are also effective; i.e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance. We consider unknown environments modeled as Markov decision processes. We propose a model-free reinforcement learning algorithm that learns a policy that first maximizes the probability of ensuring safety, then the probability of satisfying the given LTL specification and lastly, the sum of discounted Quality of Control rewards. Finally, we illustrate applicability of our RL-based approach.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 54 references that are in the map. First posted on arXiv 26 Mar 2021.

Builds on

21 of 54 references.

References · 21

  1. 2017Safe Reinforcement Learning via Shieldingcited by 427
  2. 2014A Survey of Multi-Objective Sequential Decision-Makingcited by 229
  3. 2016Reinforcement Learning With Temporal Logic Rewardscited by 114
  4. 2018On Learning Intrinsic Rewards for Policy Gradient Methodscited by 100
  5. 2019Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guaranteescited by 77
  6. 2018Omega-Regular Objectives in Model-Free Reinforcement Learningcited by 74
  7. 2019Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learningcited by 74
  8. 2021Safe Multi-Agent Reinforcement Learning via Shieldingcited by 60
  9. 2014Probably Approximately Correct MDP Learning and Control With Temporal Logic Constraintscited by 53
  10. 2014A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specificationscited by 49
  11. 2014Verification of Markov Decision Processes using Learning Algorithmscited by 38
  12. 2019Reinforcement Learning with Probabilistic Guarantees for Autonomous Drivingcited by 27
9 more
  1. 2021Multi-Agent Reinforcement Learning with Temporal Logic Specificationscited by 27
  2. 2019Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functionscited by 20
  3. 2020Reinforcement Learning of Control Policy for Linear Temporal Logic Specifications Using Limit-Deterministic Generalized B"uchi Automatacited by 11
  4. 2018Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraintscited by 8
  5. 2018Safe Reinforcement Learning via Probabilistic Shieldscited by 7
  6. 2020Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectivescited by 6
  7. 2021Probabilistically Guaranteed Satisfaction of Temporal Logic Constraints During Reinforcement Learningcited by 6
  8. 2020Synthesis of Discounted-Reward Optimal Policies for Markov Decision Processes Under Linear Temporal Logic Specificationscited by 3
  9. 2020Stochastic Games with Lexicographic Reachability-Safety Objectivescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2023Omega-Regular Reward Machinescited by 2
  2. 2025Accelerated Learning with Linear Temporal Logic using Differentiable Simulationcited by 1
  3. 2023Omega-Regular Decision Processescited by 0
  4. 2025Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectivescited by 0
  5. 2026Reward Machines for Signal Temporal Logiccited by 0