  Humans and other intelligent animals evolved highly sophisticated perception
systems that combine multiple sensory modalities. On the other hand,
state-of-the-art artificial agents rely mostly on visual inputs or structured
low-dimensional observations provided by instrumented environments. Learning to
act based on combined visual and auditory inputs is still a new topic of
research that has not been explored beyond simple scenarios. To facilitate
progress in this area we introduce a new version of VizDoom simulator to create
a highly efficient learning environment that provides raw audio observations.
We study the performance of different model architectures in a series of tasks
that require the agent to recognize sounds and execute instructions given in
natural language. Finally, we train our agent to play the full game of Doom and
find that it can consistently defeat a traditional vision-based adversary. We
are currently in the process of merging the augmented simulator with the main
ViZDoom code repository. Video demonstrations and experiment code can be found
at https://sites.google.com/view/sound-rl.
