  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.
