Emergent Behaviors in Multi-Agent Target Acquisition
This work addresses the challenge of predicting and coordinating agent behaviors in multi-agent systems, but it is incremental as it builds on existing simulation and classification techniques.
The paper tackles the problem of understanding agent behaviors in multi-agent target acquisition by simulating a pursuit-evasion game with reinforcement learning and analytical strategies, resulting in a method to categorize and classify evader behaviors using heatmaps and feature sets.
Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS). We simulate a MAS using Reinforcement Learning (RL) in a pursuit-evasion (a.k.a predator-prey pursuit) game, which shares task goals with target acquisition, and we create different adversarial scenarios by replacing RL-trained pursuers' policies with two distinct (non-RL) analytical strategies. Using heatmaps of agents' positions (state-space variable) over time, we are able to categorize an RL-trained evader's behaviors. The novelty of our approach entails the creation of an influential feature set that reveals underlying data regularities, which allow us to classify an agent's behavior. This classification may aid in catching the (enemy) targets by enabling us to identify and predict their behaviors, and when extended to pursuers, this approach towards identifying teammates' behavior may allow agents to coordinate more effectively.