Building on our recent research on neural heuristic quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a general emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem can be solved by model predictive control (MPC), but because the optimization step involves integer programming, the approach suffers from combinatorial complexity when the number of input channels becomes large. Even if we collect data points to train a neural network simultaneously, collection of training data and the training itself are still time-consuming. Therefore, we propose a general Deep Q Network (DQN) algorithm that can not only learn the trajectory but also exhibit the advantages of resilience to channel dropout. Furthermore, to transfer the model to other emulation problems, a mapping-based transfer learning approach can be used directly on the current model to obtain the optimal direction for the new emulation problems.

翻译：在最近关于神经启发式量化系统研究的基础上，我们报告了量化运动学习及对信道中断鲁棒性的成果。提出了一种符合类神经范式的通用仿真问题。该最优量化问题可通过模型预测控制（MPC）求解，但由于优化步骤涉及整数规划，当输入通道数量增大时，该方法面临组合爆炸复杂性。即使我们同时收集数据点来训练神经网络，训练数据的采集及训练过程本身仍然耗时。因此，我们提出一种通用深度Q网络（DQN）算法，该算法不仅能学习轨迹，还能展现出对信道中断的鲁棒性优势。此外，为将模型迁移至其他仿真问题，可直接在当前模型上应用基于映射的迁移学习方法，从而获取新仿真问题的最优方向。