  We present VisualHints, a novel environment for multimodal reinforcement
learning (RL) involving text-based interactions along with visual hints
(obtained from the environment). Real-life problems often demand that agents
interact with the environment using both natural language information and
visual perception towards solving a goal. However, most traditional RL
environments either solve pure vision-based tasks like Atari games or
video-based robotic manipulation; or entirely use natural language as a mode of
interaction, like Text-based games and dialog systems. In this work, we aim to
bridge this gap and unify these two approaches in a single environment for
multimodal RL. We introduce an extension of the TextWorld cooking environment
with the addition of visual clues interspersed throughout the environment. The
goal is to force an RL agent to use both text and visual features to predict
natural language action commands for solving the final task of cooking a meal.
We enable variations and difficulties in our environment to emulate various
interactive real-world scenarios. We present a baseline multimodal agent for
solving such problems using CNN-based feature extraction from visual hints and
LSTMs for textual feature extraction. We believe that our proposed
visual-lingual environment will facilitate novel problem settings for the RL
community.
