mlsanityData to 7 Oct 2026

Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning

arXiv 2203.16464 ↗First posted 30 Mar 2022cs.LG cs.AIcited by 2

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Artificial intelligence, particularly through recent advancements in deep learning, has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision. In addition to desirable evaluation metrics, a high level of interpretability is often required for these models to be reliably utilized. Therefore, explanations that offer insight into the process by which a model maps its inputs onto its outputs are much sought-after. Unfortunately, the current black box nature of machine learning models is still an unresolved issue and this very nature prevents researchers from learning and providing explicative descriptions for a model's behavior and final predictions. In this work, we propose a novel framework utilizing Adversarial Inverse Reinforcement Learning that can provide global explanations for decisions made by a Reinforcement Learning model and capture intuitive tendencies that the model follows by summarizing the model's decision-making process.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 24 of its 55 references that are in the map. First posted on arXiv 30 Mar 2022.

Builds on

24 of 55 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018Adversarial Imitation via Variational Inverse Reinforcement Learning17 Sep

Other references · 23

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  5. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  6. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  7. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  8. 2018Sanity Checks for Saliency Mapscited by 1,149
  9. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  10. 2015Teaching Machines to Read and Comprehendcited by 1,039
  11. 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 611
  12. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
11 more
  1. 2017A Deep Reinforced Model for Abstractive Summarizationcited by 364
  2. 2020UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Trainingcited by 204
  3. 2017Visualizing Deep Neural Network Decisions: Prediction Difference Analysiscited by 166
  4. 2020Drug discovery with explainable artificial intelligencecited by 118
  5. 2017Interpretable & Explorable Approximations of Black Box Modelscited by 54
  6. 2014Interpreting Tree Ensembles with inTreescited by 37
  7. 2019Generation of Policy-Level Explanations for Reinforcement Learningcited by 37
  8. 2019Exploring Computational User Models for Agent Policy Summarizationcited by 29
  9. 2021Resolution-Based Distillation for Efficient Histology Image Classificationcited by 10
  10. 2022Interpretable Concept-based Prototypical Networks for Few-Shot Learningcited by 4
  11. 2019Interpretable Few-Shot Learning via Linear Distillationcited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Explaining Imitation Learning through Framescited by 1
  2. 2024Interpretable Robotic Manipulation from Languagecited by 0

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  6. 2024Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
  7. 2025Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning
  8. 2024Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium Proof