The Impact of Missing Velocity Information in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2112.12465v1
• Date: Thu, 23 Dec 2021 11:07:00 GMT
• Title: The Impact of Missing Velocity Information in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning
• Authors: Fabian Hart, Martin Waltz, Ostap Okhrin
• Abstract summary: We introduce a novel approach to dynamic obstacle avoidance based on Deep Reinforcement Learning. We thoroughly investigate the effect of missing velocity information on an agent's performance in obstacle avoidance tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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