We examine the problem of learning to cooperate in the context of wireless communication. In our setting, two agents must learn modulation schemes that enable them to communicate across a power-constrained additive white Gaussian noise channel. We investigate whether learning is possible under different levels of information sharing between distributed agents which are not necessarily co-designed. We employ the "Echo" protocol, a "blind" interactive learning protocol where an agent hears, understands, and repeats (echoes) back the message received from another agent, simultaneously training itself to communicate. To capture the idea of cooperation between "not necessarily co-designed" agents we use two different populations of function approximators - neural networks and polynomials. We also include interactions between learning agents and non-learning agents with fixed modulation protocols such as QPSK and 16QAM. We verify the universality of the Echo learning approach, showing it succeeds independent of the inner workings of the agents. In addition to matching the communication expectations of others, we show that two learning agents can collaboratively invent a successful communication approach from independent random initializations. We complement our simulations with an implementation of the Echo protocol in software-defined radios. To explore the continuum of co-design, we study how learning is impacted by different levels of information sharing between agents, including sharing training symbols, losses, and full gradients. We find that co-design (increased information sharing) accelerates learning. Learning higher order modulation schemes is a more difficult task, and the beneficial effect of co-design becomes more pronounced as the task becomes harder.

中文翻译：

---

调制方案的盲交互学习：无需协同设计的多智能体合作

我们研究了在无线通信环境中学习合作的问题。在我们的设置中，两个代理必须学习调制方案，使他们能够通过功率受限的加性高斯白噪声通道进行通信。我们调查了在不一定是共同设计的分布式代理之间的不同信息共享级别下学习是否可能。我们采用“回声”协议，一种“盲”交互式学习协议，其中一个代理听到、理解并重复（回声）从另一个代理收到的消息，同时训练自己进行交流。为了捕捉“不一定是共同设计的”代理之间合作的想法，我们使用了两种不同的函数逼近器群体——神经​​网络和多项式。我们还包括具有固定调制协议（例如 QPSK 和 16QAM）的学习代理和非学习代理之间的交互。我们验证了 Echo 学习方法的普遍性，表明它的成功独立于代理的内部工作。除了匹配其他人的交流期望之外，我们还表明两个学习代理可以从独立的随机初始化中协作发明一种成功的交流方法。我们通过在软件定义无线电中实现 Echo 协议来补充我们的模拟。为了探索协同设计的连续性，我们研究了智能体之间不同级别的信息共享如何影响学习，包括共享训练符号、损失和完整梯度。我们发现协同设计（增加信息共享）可以加速学习。