Investigating Symbiosis in Robotic Ecosystems: A Case Study for Multi-Robot Reinforcement Learning Reward Shaping

This paper presents a bio-inspired reward shaping approach for multi-agent reinforcement learning (MARL) in heterogeneous multi-robot systems, leveraging a formal symbiosis model to enhance cooperation. We categorize interactions based on mutualism, commensalism, and parasitism, introducing constructs such as graph models, state transition systems, and resource flow models to characterize inter-agent dependencies. By incorporating a taxonomy of symbiotic relationships into MARL, we define reward structures that reinforce cooperative behavior in complex tasks. Our experimental results demonstrate that while traditional rewards suffice for simple tasks like CartPendulum, mutualistic rewards provide qualitative benefits in high-dimensional tasks such as ShadowHand Object Passing and Mobile Manipulation, including increased learning stability, smoother convergence, and reduced performance variance. These findings suggest that symbiotic reward shaping provides a structured mechanism for enhancing multi-robot cooperation, with benefits that extend beyond numerical performance metrics. Future work should explore adaptive interaction mechanisms and generalization across diverse robotic applications.