mlsanityData to 14 Oct 2026

Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents

arXiv 2207.04118 ↗First posted 8 Jul 2022cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

In this extended abstract we discuss the opportunities and challenges of studying intrinsically-motivated agents for exploration in textual environments. We argue that there is important synergy between text environments and autonomous agents. We identify key properties of text worlds that make them suitable for exploration by autonmous agents, namely, depth, breadth, progress niches and the ease of use of language goals; we identify drivers of exploration for such agents that are implementable in text worlds. We discuss the opportunities of using autonomous agents to make progress on text environment benchmarks. Finally we list some specific challenges that need to be overcome in this area.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

18 of 28 references.

References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2017Hindsight Experience Replaycited by 1,136
  3. 2018Exploration by Random Network Distillationcited by 993
  4. 2022ScienceWorld: Is your Agent Smarter than a 5th Grader?cited by 327
  5. 2018TextWorld: A Learning Environment for Text-based Gamescited by 310
  6. 2020First return, then explorecited by 266
  7. 2019Self-Supervised Exploration via Disagreementcited by 265
  8. 2019Interactive Fiction Games: A Colossal Adventurecited by 168
  9. 2020Automatic Curriculum Learning For Deep RL: A Short Surveycited by 150
  10. 2020Keep CALM and Explore: Language Models for Action Generation in Text-based Gamescited by 108
  11. 2020Learning with AMIGo: Adversarially Motivated Intrinsic Goalscited by 88
  12. 2017Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learningcited by 83
6 more
  1. 2018CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learningcited by 79
  2. 2020Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Explorationcited by 68
  3. 2020Grounded Language Learning Fast and Slowcited by 54
  4. 2022Semantic Exploration from Language Abstractions and Pretrained Representationscited by 51
  5. 2022Improving Intrinsic Exploration with Language Abstractionscited by 49
  6. 2020Exploration Based Language Learning for Text-Based Gamescited by 18

Built on by

Not cited yet.