# An Analysis of Deep Reinforcement Learning Agents for Text-based Games

arXiv: https://arxiv.org/abs/2209.04105 · HTML: https://mlsanity.com/paper/2209.04105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Keep CALM and Explore: Language Models for Action Generation in Text-based Games](https://mlsanity.com/paper/2010.02903/) (2020)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning](https://mlsanity.com/paper/1809.02121/) (2018)
- [Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning](https://mlsanity.com/paper/1812.01628/) (2018)
- [Learning Dynamic Belief Graphs to Generalize on Text-Based Games](https://mlsanity.com/paper/2002.09127/) (2020)
- [How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds](https://mlsanity.com/paper/2006.07409/) (2020)
- [Counting to Explore and Generalize in Text-based Games](https://mlsanity.com/paper/1806.11525/) (2018)
- [Learning Knowledge Graph-based World Models of Textual Environments](https://mlsanity.com/paper/2106.09608/) (2021)
- [Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games](https://mlsanity.com/paper/2010.11655/) (2020)
- [Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents](https://mlsanity.com/paper/2103.13552/) (2021)
- [Interactive Language Learning by Question Answering](https://mlsanity.com/paper/1908.10909/) (2019)
- [LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games](https://mlsanity.com/paper/1909.01646/) (2019)
- [Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge](https://mlsanity.com/paper/2005.00811/) (2020)
- [Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction](https://mlsanity.com/paper/1911.12511/) (2019)
- [Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior](https://mlsanity.com/paper/2104.09469/) (2021)
- [Comprehensible Context-driven Text Game Playing](https://mlsanity.com/paper/1905.02265/) (2019)
- [Learn 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 Games](https://mlsanity.com/paper/1908.04777/) (2019)
- [Learning to Generalize for Sequential Decision Making](https://mlsanity.com/paper/2010.02229/) (2020)

## Built on by (most cited first, 1 of 1)

- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)

## 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.