Learning to Communicate in a Noisy Environment
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In this work we examine the problem of learning to cooperate in the context of wireless communication. We consider the two agent setting where agents must learn modulation and demodulation schemes that enable them to communicate with each other in the presence of a power-constrained additive white Gaussian noise (AWGN) channel. We investigate whether learning is possible under different levels of information sharing between distributed agents that are not necessarily co-designed. We make use of the "Echo" protocol, a 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 agents that are "not necessarily co-designed," we use two different populations of function approximators - neural networks and polynomials. In addition to diverse learning agents, we include non-learning agents that use fixed modulation protocols such as QPSK and 16QAM. We verify that the Echo learning approach succeeds independent of the inner workings of the agents, and that learning agents can not only learn to match the communication expectations of others, but can also 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 between tight co-design of learning agents and independently designed agents, we study how learning is impacted by different levels of information sharing - including sharing training symbols, sharing intermediate loss information, and sharing full gradient information. We find that, in general, co-design (increased information sharing) accelerates learning and that this effect becomes more pronounced as the communication task becomes harder.
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