text-based games
35 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
Language Understanding for Text-based Games Using Deep Reinforcement Learning
MikulasZelinka/pyfiction
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EMNLP 2015
We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations.
Deep Reinforcement Learning with a Natural Language Action Space
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.
Using reinforcement learning to learn how to play text-based games
The ability to learn optimal control policies in systems where action space is defined by sentences in natural language would allow many interesting real-world applications such as automatic optimisation of dialogue systems.
Counting to Explore and Generalize in Text-based Games
xingdi-eric-yuan/TextWorld-Coin-Collector
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We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments.
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making.
TextWorldExpress: Simulating Text Games at One Million Steps Per Second
Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning.
Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games
yukw777/temporal-discrete-graph-updater
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In order to address DGU's weaknesses while preserving its high interpretability, we propose the Temporal Discrete Graph Updater (TDGU), a novel neural network model that represents dynamic knowledge graphs as a sequence of timestamped graph events and models them using a temporal point based graph neural network.
TextWorld: A Learning Environment for Text-based Games
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games.
Towards Solving Text-based Games by Producing Adaptive Action Spaces
To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success.
Building Dynamic Knowledge Graphs from Text-based Games
We are interested in learning how to update Knowledge Graphs (KG) from text.
