We propose for risk-sensitive control of finite Markov chains a counterpart of the popular Q-learning algorithm for classical Markov decision processes. The algorithm is shown to converge with ...

The network is trained using the Q-learning algorithm, then fine-tuned and quantized to low-bit (8-bit) precision for embedded deployment on the SpiNNaker2 neuromorphic chip.