  We introduce a novel approach to dynamic obstacle avoidance based on Deep
Reinforcement Learning by defining a traffic type independent environment with
variable complexity. Filling a gap in the current literature, we thoroughly
investigate the effect of missing velocity information on an agent's
performance in obstacle avoidance tasks. This is a crucial issue in practice
since several sensors yield only positional information of objects or vehicles.
We evaluate frequently-applied approaches in scenarios of partial
observability, namely the incorporation of recurrency in the deep neural
networks and simple frame-stacking. For our analysis, we rely on
state-of-the-art model-free deep RL algorithms. The lack of velocity
information is found to significantly impact the performance of an agent. Both
approaches - recurrency and frame-stacking - cannot consistently replace
missing velocity information in the observation space. However, in simplified
scenarios, they can significantly boost performance and stabilize the overall
training procedure.
