We present ScienceWorld, a benchmark to test agents' scientific reasoning abilities in a new
interactive text environment at the level of a standard elementary school science curriculum …

Text-based games have emerged as an important test-bed for Reinforcement Learning (RL)
research, requiring RL agents to combine grounded language understanding with …

We focus on creating agents that act in alignment with socially beneficial norms and values
in interactive narratives or text-based games--environments wherein an agent perceives and …

We seek to create agents that both act and communicate with other agents in pursuit of a
goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)--a large-scale crowd …

World models improve a learning agent's ability to efficiently operate in interactive and
situated environments. This work focuses on the task of building world models of text-based …

We study reinforcement learning (RL) for text-based games, which are interactive
simulations in the context of natural language. While different methods have been …

Text-based games are long puzzles or quests, characterized by a sequence of sparse and
potentially deceptive rewards. They provide an ideal platform to develop agents that …

A suitable state representation is a fundamental part of the learning process in
Reinforcement Learning. In various tasks, the state can either be described by natural …

Text Worlds are virtual environments for embodied agents that, unlike 2D or 3D
environments, are rendered exclusively using textual descriptions. These environments offer …

We focus on the task of creating a reinforcement learning agent that is inherently
explainable---with the ability to produce immediate local explanations by thinking out loud …