In collaborative goal-oriented settings, the participants are not only interested in achieving a successful outcome, but do also implicitly negotiate the effort they put into the interaction (by adapting to each other). In this work, we propose a challenging interactive reference game that requires two players to coordinate on vision and language observations. The learning signal in this game is a score (given after playing) that takes into account the achieved goal and the players' assumed efforts during the interaction. We show that a standard Proximal Policy Optimization (PPO) setup achieves a high success rate when bootstrapped with heuristic partner behaviors that implement insights from the analysis of human-human interactions. And we find that a pairing of neural partners indeed reduces the measured joint effort when playing together repeatedly. However, we observe that in comparison to a reasonable heuristic pairing there is still room for improvement -- which invites further research in the direction of cost-sharing in collaborative interactions.

在协作目标导向的情境中，我们提出了一个具有挑战性的交互参考游戏，要求两名玩家在视觉和语言观察方面进行协调。我们展示了标准的Proximal Policy Optimization (PPO)设置在启发式伙伴行为的引导下能够获得较高的成功率，并且我们发现相互配对的神经伙伴确实在重复游戏时减少了测量到的联合努力，但与合理的启发式配对相比仍有改进的空间，这促使进一步探索协作互动中的成本分享方向的研究。

成功的成本共担：一种评估与学习多智能体协作指导与遵循策略的游戏