Unveiling Complex Collective Behaviors from Simple Rewards
arXiv paper 'Unveiling Complex Collective Behaviors from Simple Rewards' proposes the EEC explanation framework and Agent Response Map (ARM) tool to reveal the mechanism by which complex collective behaviors emerge from simple rewards in multi-agent reinforcement learning (MARL). ARM can identify agents' aggregation and avoidance regions, and discovers that robots implicitly learn the geometric field of the environment as a target for coordinated movement. The framework is validated in two tasks: cooperative multi-robot shape assembly and competitive predator-prey pursuit.
arXiv paper proposes the EEC framework and ARM tool to explain complex collective behaviors emerging from simple rewards in MARL, validated in two tasks.
The ARM tool, by analyzing agents' decision patterns, reveals their implicit learning of the environment's geometric field, providing a new method for explainable MARL. Next steps could verify whether ARM applies to more complex environments or real robots.
This research provides an analytical tool for the interpretability of multi-robot systems, potentially advancing the application of MARL in robot swarms.
The framework can reduce debugging costs in multi-robot system deployment and improve task reliability, but further validation is needed before commercial application.
In the future, ARM may be integrated into robot swarm control systems or extended to other multi-agent scenarios.