mlsanityData to 14 Oct 2026

On Generating Explanations for Reinforcement Learning Policies: An Empirical Study

arXiv 2309.16960 ↗First posted 29 Sep 2023cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Understanding a textitreinforcement learning policy, which guides state-to-action mappings to maximize rewards, necessitates an accompanying explanation for human comprehension. In this paper, we introduce a set of textitlinear temporal logic formulae designed to provide explanations for policies, and an algorithm for searching through those formulae for the one that best explains a given policy. Our focus is on explanations that elucidate both the ultimate objectives accomplished by the policy and the prerequisite conditions it upholds throughout its execution. The effectiveness of our proposed approach is illustrated through a simulated game of capture-the-flag and a car-parking environment,

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 29 Sep 2023.

Builds on

21 of 46 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  4. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  5. 2017Hindsight Experience Replaycited by 1,136
  6. 2017Deep Reinforcement Learning that Matterscited by 754
  7. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  8. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 298
  9. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  10. 2016Reinforcement Learning With Temporal Logic Rewardscited by 117
  11. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 103
  12. 2020Explainability in Deep Reinforcement Learningcited by 97
9 more
  1. 2017Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigationcited by 77
  2. 2023NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Modelscited by 59
  3. 2022From Spot 2.0 to Spot 2.10: What's New?cited by 22
  4. 2020TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environmentscited by 18
  5. 2018Information-Guided Temporal Logic Inference with Prior Knowledgecited by 13
  6. 2020Reinforcement Learning of Control Policy for Linear Temporal Logic Specifications Using Limit-Deterministic Generalized B"uchi Automatacited by 11
  7. 2021Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approachcited by 11
  8. 2022Learning Signal Temporal Logic through Neural Network for Interpretable Classificationcited by 10
  9. 2021Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networkscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026S2Act: Simple Spiking Actorcited by 0