用强化学习让智能体自动隐式修复通信错误，抗干扰更强。
Implicit Repair with Reinforcement Learning in Emergent Communication
- 通过强化学习在通信中引入冗余，隐式纠正潜在误解。
- 噪声环境下任务成功率提升，且泛化能力不下降。
- 适合需要鲁棒通信的多智能体系统设计者参考。
对话修复是多智能体交互中检测并解决误传与误导信息的机制。本文探讨了涌现通信中一种未充分研究的隐式修复方式：对话方有意以特定方式传递信息，防止其他对话方产生误解。我们通过在经典的刘易斯信号博弈（Lewis Game）中引入通信信道和输入噪声，研究冗余如何改变通信协议，使其在外部环境压力下仍能完成任务。实验表明，智能体自发增加消息冗余以抵御噪声影响，保障任务成功率。此外，新兴通信协议的泛化性能与完全确定性环境下的基线架构相当。本方法是唯一能在有无噪声场景下均保持高泛化性能的方案，适用于构建稳健通信系统。
原文摘要 · Abstract (English)
Conversational repair is a mechanism used to detect and resolve miscommunication and misinformation problems when two or more agents interact. One particular and underexplored form of repair in emergent communication is the implicit repair mechanism, where the interlocutor purposely conveys the desired information in such a way as to prevent misinformation from any other interlocutor. This work explores how redundancy can modify the emergent communication protocol to continue conveying the necessary information to complete the underlying task, even with additional external environmental pressures such as noise. We focus on extending the signaling game, called the Lewis Game, by adding noise in the communication channel and inputs received by the agents. Our analysis shows that agents add redundancy to the transmitted messages as an outcome to prevent the negative impact of noise on the task success. Additionally, we observe that the emerging communication protocol's generalization capabilities remain equivalent to architectures employed in simpler games that are entirely deterministic. Additionally, our method is the only one suitable for producing robust communication protocols that can handle cases with and without noise while maintaining increased generalization performance levels.
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