Spiking neural networks (SNNs) are attractive for energy-constrained reinforcement learning because they compute sparsely and in an event-driven manner. In value-based RL, deep spiking Q-networks (DSQNs) combine these benefits with Q-learning. However, a new preprint highlights a specific failure mode: common-mode errors that limit performance when the network operates at low timesteps.
The paper, posted on arXiv, shows that these errors—shared across neurons or channels—become pronounced as the number of timesteps shrinks. At low timesteps, the network has fewer opportunities to average out noise, so systematic biases dominate and degrade the Q-value estimates. The authors argue this is a fundamental constraint for DSQNs, not just a tuning issue.
Because the source is a single preprint, the findings are not yet independently verified. The abstract does not provide experimental details or proposed fixes, so the practical severity and possible mitigations remain open questions. Still, the work points to a concrete obstacle for deploying spiking RL agents on energy-limited hardware.