A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge
This addresses collision avoidance in UAV swarms, which is a domain-specific problem for robotics and autonomous systems, but it appears incremental as it builds on existing MARL methods with a novel reward design.
The paper tackles cooperative collision avoidance for UAV swarms by introducing a multi-agent reinforcement learning framework that uses domain knowledge from image processing to design rewards, resulting in minimized agent interactions and elimination of complex credit assignment schemes, with extensive experiments showing performance against state-of-the-art algorithms.
This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UAV swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UAVs obtain the ability to adapt to complex environments where contours may be non-viable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms.