  Development of navigation algorithms is essential for the successful
deployment of robots in rapidly changing hazardous environments for which prior
knowledge of configuration is often limited or unavailable. Use of traditional
path-planning algorithms, which are based on localization and require detailed
obstacle maps with goal locations, is not possible. In this regard,
vision-based algorithms hold great promise, as visual information can be
readily acquired by a robot's onboard sensors and provides a much richer source
of information from which deep neural networks can extract complex patterns.
Deep reinforcement learning has been used to achieve vision-based robot
navigation. However, the efficacy of these algorithms in environments with
dynamic obstacles and high variation in the configuration space has not been
thoroughly investigated. In this paper, we employ a deep Dyna-Q learning
algorithm for room evacuation and obstacle avoidance in partially observable
environments based on low-resolution raw image data from an onboard camera. We
explore the performance of a robotic agent in environments containing no
obstacles, convex obstacles, and concave obstacles, both static and dynamic.
Obstacles and the exit are initialized in random positions at the start of each
episode of reinforcement learning. Overall, we show that our algorithm and
training approach can generalize learning for collision-free evacuation of
environments with complex obstacle configurations. It is evident that the agent
can navigate to a goal location while avoiding multiple static and dynamic
obstacles, and can escape from a concave obstacle while searching for and
navigating to the exit.
