mlsanityData to 14 Oct 2026

Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces

arXiv 1905.09700 ↗First posted 23 May 2019cs.LGcited by 7

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve text-based games with combinatorial action spaces. Specifically, we introduce a new compressed sensing algorithm, named IK-OMP, which can be seen as an extension to the Orthogonal Matching Pursuit (OMP). We incorporate IK-OMP into a supervised imitation learning setting and show that the combined approach (Sparse Imitation Learning, Sparse-IL) solves the entire text-based game of Zork1 with an action space of approximately 10 million actions given both perfect and noisy demonstrations.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 44 references that are in the map. First posted on arXiv 23 May 2019.

Builds on

14 of 44 references.

References · 14

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  2. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
  3. 2015Sequence Level Training with Recurrent Neural Networkscited by 576
  4. 2018TextWorld: A Learning Environment for Text-based Gamescited by 310
  5. 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 161
  6. 2016An Actor-Critic Algorithm for Sequence Predictioncited by 159
  7. 2015Deep Reinforcement Learning with a Natural Language Action Spacecited by 88
  8. 2018Learn What Not to Learn: Action Elimination with Deep Reinforcement Learningcited by 77
  9. 2015Language Understanding for Text-based Games Using Deep Reinforcement Learningcited by 71
  10. 2018Counting to Explore and Generalize in Text-based Gamescited by 36
  11. 2014Optimal Demand Response Using Device Based Reinforcement Learningcited by 7
  12. 2018Towards Solving Text-based Games by Producing Adaptive Action Spacescited by 5
2 more
  1. 2018Using reinforcement learning to learn how to play text-based gamescited by 3
  2. 2016Compressed Sensing for Finite-Valued Signalscited by 2

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 1.

7 papers.

  1. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 24
  2. 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environmentscited by 17
  3. 2023Knowledge-enhanced Agents for Interactive Text Gamescited by 10
  4. 2024Stochastic Q-learning for Large Discrete Action Spacescited by 5
  5. 2024A Survey on Reinforcement Learning Applications in SLAMcited by 3
  6. 2025Language Model Distillation: A Temporal Difference Imitation Learning Perspectivecited by 2
  7. 2022Affordance Extraction with an External Knowledge Database for Text-Based Simulated Environmentscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2020Compressed imitation learning
  2. 2019Reinforced Imitation in Heterogeneous Action Space
  3. 2026Greedy Is Enough: Sparse Action Discovery in Agentic LLMs
  4. 2021Provable Representation Learning for Imitation with Contrastive Fourier Features
  5. 2024Robust Imitation Learning for Automated Game Testing
  6. 2021Modeling Strong and Human-Like Gameplay with KL-Regularized Search
  7. 2024Efficient Active Imitation Learning with Random Network Distillation
  8. 2022Self-Imitation Learning from Demonstrations