An Analysis of Deep Reinforcement Learning Agents for Text-based Games
Not on the map. Why?
Abstract (arXiv)
Text-based games(TBG) are complex environments which allow users or computer agents to make textual interactions and achieve game goals.In TBG agent design and training process, balancing the efficiency and performance of the agent models is a major challenge. Finding TBG agent deep learning modules' performance in standardized environments, and testing their performance among different evaluation types is also important for TBG agent research. We constructed a standardized TBG agent with no hand-crafted rules, formally categorized TBG evaluation types, and analyzed selected methods in our environment.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 51 references that are in the map. First posted on arXiv 9 Sep 2022.
Builds on
30 of 51 references.
References · 30
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2014Going Deeper with Convolutionscited by 4,231
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
- 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
- 2018TextWorld: A Learning Environment for Text-based Gamescited by 304
- 2017Advances in Pre-Training Distributed Word Representationscited by 207
- 2019Interactive Fiction Games: A Colossal Adventurecited by 165
- 2019Learning to Speak and Act in a Fantasy Text Adventure Gamecited by 122
18 more
- 2020Keep CALM and Explore: Language Models for Action Generation in Text-based Gamescited by 108
- 2020Graph Constrained Reinforcement Learning for Natural Language Action Spacescited by 80
- 2018Learn What Not to Learn: Action Elimination with Deep Reinforcement Learningcited by 77
- 2018Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learningcited by 53
- 2020Learning Dynamic Belief Graphs to Generalize on Text-Based Gamescited by 49
- 2020How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worldscited by 38
- 2018Counting to Explore and Generalize in Text-based Gamescited by 36
- 2021Learning Knowledge Graph-based World Models of Textual Environmentscited by 31
- 2020Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Gamescited by 22
- 2021Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agentscited by 20
- 2019Interactive Language Learning by Question Answeringcited by 19
- 2019LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Gamescited by 17
- 2020Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledgecited by 16
- 2019Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fictioncited by 14
- 2021Training Value-Aligned Reinforcement Learning Agents Using a Normative Priorcited by 14
- 2019Comprehensible Context-driven Text Game Playingcited by 10
- 2019Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Gamescited by 7
- 2020Learning to Generalize for Sequential Decision Makingcited by 5
Built on by
Citing papers per year: 2023: 1.
1 paper.