Discretization of analog communication signals by noise addition in reinforcement learning of communication
نویسنده
چکیده
Towards the unified processing of symbols and patterns by neural networks, it was examined that symbols emerge using neural networks that is trained only by reinforcement learning. A very simple communication-learning task was assumed, and some noise is added to the communication signals. After learning, as the noise level during learning became larger, the communication signals were binarized more, and the system became more tolerant of noise unless the noise level was too large. The receiver was also trying to interpret the signals as binarized value. Furthermore, it was examined that recurrent neural networks promote the discretization.
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