学习说什么以及多精确：通过可微分离散通信学习实现高效通信
Learning what to say and how precisely: Efficient Communication via Differentiable Discrete Communication Learning
AI总结：
针对MARL中带宽受限的通信问题，提出可微分离散通信学习框架DDCL，通过扩展支持无界信号实现即插即用，使智能体能动态调整消息精度，在匹配或超越任务性能的同时将带宽降低超一个数量级。
摘要：
多智能体强化学习（MARL）中的有效通信对于成功至关重要，但受到带宽的限制，然而过去的方法仅限于决定是否通信的复杂门控机制，而非通信的精确程度。在比特级优化消息精度从根本上来说更难，因为所需的离散化步骤会破坏梯度流。我们通过推广可微分离散通信学习（DDCL，一种用于离散消息端到端优化的框架）来解决这一问题。我们的主要贡献是扩展DDCL以支持无界信号，将其转化为适用于任何MARL架构的通用即插即用层。我们用三个关键结果验证了该方法。首先，通过在受控环境中的定性分析，我们展示了智能体如何学习根据任务的信息需求动态调整消息精度。其次，我们将DDCL变体集成到四种最先进的MARL算法中，表明它将带宽减少了超过一个数量级，同时匹配或超越了任务性能。最后，我们为MARL通信中的“苦涩的教训”提供了直接证据：利用DDCL的简单基于Transformer的策略匹配了复杂、专用架构的性能，质疑了定制通信设计的必要性。
英文摘要：
Effective communication in multi-agent reinforcement learning (MARL) is critical for success but constrained by bandwidth, yet past approaches have been limited to complex gating mechanisms that only decide \textit{whether} to communicate, not \textit{how precisely}. Learning to optimize message precision at the bit-level is fundamentally harder, as the required discretization step breaks gradient flow. We address this by generalizing Differentiable Discrete Communication Learning (DDCL), a framework for end-to-end optimization of discrete messages. Our primary contribution is an extension of DDCL to support unbounded signals, transforming it into a universal, plug-and-play layer for any MARL architecture. We verify our approach with three key results. First, through a qualitative analysis in a controlled environment, we demonstrate \textit{how} agents learn to dynamically modulate message precision according to the informational needs of the task. Second, we integrate our variant of DDCL into four state-of-the-art MARL algorithms, showing it reduces bandwidth by over an order of magnitude while matching or exceeding task performance. Finally, we provide direct evidence for the \enquote{Bitter Lesson} in MARL communication: a simple Transformer-based policy leveraging DDCL matches the performance of complex, specialized architectures, questioning the necessity of bespoke communication designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
