  Reinforcement learning generates policies based on reward functions and
hyperparameters. Slight changes in these can significantly affect results. The
lack of documentation and reproducibility in Reinforcement learning research
makes it difficult to replicate once-deduced strategies. While previous
research has identified strategies using grounded maneuvers, there is limited
work in more complex environments. The agents in this study are simulated
similarly to Open Al's hider and seek agents, in addition to a flying
mechanism, enhancing their mobility, and expanding their range of possible
actions and strategies. This added functionality improves the Hider agents to
develop a chasing strategy from approximately 2 million steps to 1.6 million
steps and hiders

Artificial Intelligence;Machine Learning;Multiagent Systems;Robotics