mlsanityData to 14 Oct 2026

Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

arXiv 2010.11655 ↗First posted 22 Oct 2020cs.LG cs.AIcited by 22

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Abstract (arXiv)

We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2020.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 7; 2024: 0; 2025: 2.

22 papers.

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  1. 2021Hierarchical Learning for Generation with Long Source Sequencescited by 32
  2. 2021DAGN: Discourse-Aware Graph Network for Logical Reasoningcited by 28
  3. 2023Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Gamecited by 28
  4. 2022Multi-Stage Episodic Control for Strategic Exploration in Text Gamescited by 22
  5. 2025Monte Carlo Planning with Large Language Model for Text-Based Game Agentscited by 22
  6. 2021Pre-trained Language Models as Prior Knowledge for Playing Text-based Gamescited by 21
  7. 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environmentscited by 17
  8. 2021Inherently Explainable Reinforcement Learning in Natural Languagecited by 16
  9. 2021Training Value-Aligned Reinforcement Learning Agents Using a Normative Priorcited by 14
  10. 2021A Survey of Text Games for Reinforcement Learning informed by Natural Languagecited by 14
  11. 2021Generalization in Text-based Games via Hierarchical Reinforcement Learningcited by 11
  12. 2022Perceiving the World: Question-guided Reinforcement Learning for Text-based Gamescited by 10
10 more
  1. 2022Behavior Cloned Transformers are Neurosymbolic Reasonerscited by 10
  2. 2025Multi-Turn Puzzles: Evaluating Interactive Reasoning and Strategic Dialogue in LLMscited by 10
  3. 2023Dialogue Shaping: Empowering Agents through NPC Interactioncited by 8
  4. 2023Story Shaping: Teaching Agents Human-like Behavior with Storiescited by 7
  5. 2022A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challengescited by 3
  6. 2023Language Decision Transformers with Exponential Tilt for Interactive Text Environmentscited by 3
  7. 2023A Minimal Approach for Natural Language Action Space in Text-based Gamescited by 3
  8. 2023Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learningcited by 2
  9. 2022An Analysis of Deep Reinforcement Learning Agents for Text-based Gamescited by 1
  10. 2023Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Gamescited by 1