Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details
A new evaluation scheme, cross-implementation cross-play, is introduced to test zero-shot coordination (ZSC) algorithms by varying implementation details beyond random seeds, addressing robustness to specification ambiguities.
Zero-shot coordination (ZSC) algorithms aim to enable independently engineered AI agents to coordinate without prior interaction. Current evaluations rely on single implementations with different random seeds, neglecting variations from independent interpretations. This work systematically evaluates ZSC robustness by introducing cross-implementation cross-play, which varies implementation details known to affect multi-agent reinforcement learning performance.
The cross-implementation cross-play scheme exposes ZSC algorithms to implementation variations such as neural network architecture and hyperparameters, revealing brittleness not captured by inter-seed cross-play alone.
Robustness to implementation details is critical for deploying ZSC in real-world multi-agent systems where independent teams build agents from shared specifications.
Improved ZSC robustness can reduce integration costs and failures in multi-vendor AI systems, enabling safer autonomous coordination in logistics, robotics, and customer service.
Future ZSC research may adopt cross-implementation evaluation as a standard, leading to more reliable coordination algorithms. Watch for benchmarks incorporating diverse implementations.