  Live fire creates a dynamic, rapidly changing environment that presents a
worthy challenge for deep learning and artificial intelligence methodologies to
assist firefighters with scene comprehension in maintaining their situational
awareness, tracking and relay of important features necessary for key decisions
as they tackle these catastrophic events. We propose a deep Q-learning based
agent who is immune to stress induced disorientation and anxiety and thus able
to make clear decisions for navigation based on the observed and stored facts
in live fire environments. As a proof of concept, we imitate structural fire in
a gaming engine called Unreal Engine which enables the interaction of the agent
with the environment. The agent is trained with a deep Q-learning algorithm
based on a set of rewards and penalties as per its actions on the environment.
We exploit experience replay to accelerate the learning process and augment the
learning of the agent with human-derived experiences. The agent trained under
this deep Q-learning approach outperforms agents trained through alternative
path planning systems and demonstrates this methodology as a promising
foundation on which to build a path planning navigation assistant capable of
safely guiding fire fighters through live fire environments.
