To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the
compositional, relational, and hierarchical structure of the world, and learn to transfer it to the …

Abstract Machine Learning (ML) has become an important part of the creative process for
human fiction writers, allowing them to utilize various sources of information and be inspired …

We introduce TextWorld, a sandbox learning environment for the training and evaluation of
RL agents on text-based games. TextWorld is a Python library that handles interactive play …

In this paper, we review recent deep learning advances in the context of how they have
been applied to play different types of video games such as first-person shooters, arcade …

A hallmark of human intelligence is the ability to understand and communicate with
language. Interactive Fiction games are fully text-based simulation environments where a …

Learning how to act when there are many available actions in each state is a challenging
task for Reinforcement Learning (RL) agents, especially when many of the actions are …

Text-based games present a unique challenge for autonomous agents to operate in natural
language and handle enormous action spaces. In this paper, we propose the Contextual …

Playing text-based games requires skills in processing natural language and sequential
decision making. Achieving human-level performance on text-based games remains an …

Text-based adventure games provide a platform on which to explore reinforcement learning
in the context of a combinatorial action space, such as natural language. We present a deep …

Using the frequency of keywords is a classic approach in the formal analysis of text, but has
the drawback of glossing over the relationality of word meanings. Word embedding models …