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Studies involving the group predator behavior of wolves have inspired multiple robotic architectures to mimic these biological behaviors in their designs and research. In this work, we aim to use robotic systems to mimic wolf packs' single and group behavior. This work aims to extend the original research by Weitzenfeld et al [7] and evaluate under a new multi-robot robot system architecture. The multiple robot architecture includes a 'Prey' pursued by a wolf pack consisting of an 'Alpha' and 'Beta' robotic group. The Alpha Wolf' will be the group leader, searching and tracking the 'Prey.' At the same time, the multiple Beta 'Wolves' will follow behind the Alpha, tracking and maintaining a set distance in the formation. The robotic systems used are multiple raspberry pi-robots designed in the USF bio-robotics lab that use a combination of color cameras and distance sensors to assist the Beta 'Wolves' in keeping a set distance between the Alpha "Wolf" and themselves. Several experiments were performed in simulation, using Webots, and with physical robots. An analysis was done comparing the performance of the physical robot in the real world to the virtual robot in the simulated environment. 

In the last decade, studies have demonstrated that hippocampal place cells influence rats’ navigational learning ability. Moreover, researchers have observed that place cell sequences associated with routes leading to a reward are reactivated during rest periods. This phenomenon is known as Hippocampal Replay, which is thought to aid navigational learning and memory consolidation. These findings in neuroscience have inspired new robot navigation models that emulate the learning process of mammals. This study presents a novel model that encodes path information using place cell connections formed during online navigation. Our model employs these connections to generate sequences of state-action pairs to train our actor-critic reinforcement learning model offline. Our results indicate that our method can accelerate the learning process of solving an open-world navigational task. Specifically, we demonstrate that our approach can learn optimal paths through open-field mazes with obstacles.