text-based games
31 papers with code • 0 benchmarks • 3 datasets
Text-based games to evaluate the Reinforcement Learning Agents
Benchmarks
These leaderboards are used to track progress in text-based games
Libraries
Use these libraries to find text-based games models and implementations
Jericho
TextWorld KG
Cards Against Humanity
Most implemented papers
Learning to Follow Instructions in Text-Based Games
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language.
DiffG-RL: Leveraging Difference between State and Common Sense
ibm/diffg-rl
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We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder.
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learning
weichenli1223/text-based-adventure-games-using-sac
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To deal with sparse extrinsic rewards from the environment, we combine it with a potential-based reward shaping technique to provide more informative (dense) reward signals to the RL agent.
Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games
Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP.
A Minimal Approach for Natural Language Action Space in Text-based Games
ktr0921/tac
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Text-based games (TGs) are language-based interactive environments for reinforcement learning.
ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games
In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks.
ScriptWorld: Text Based Environment For Learning Procedural Knowledge
Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents.
Large Language Models Are Neurosymbolic Reasoners
A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning.
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
To tackle these issues, in this paper, we present EXPLORER which is an exploration-guided reasoning agent for textual reinforcement learning.
