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arXiv Machine Learning By Zijie Xu, Bingrui Guo, Yiding Sun, Yiting Dong, Zhile Yang, Zhaofei Yu

Common-Mode Errors Limit Low-Timestep Deep Spiking Q-Networks

Read the original on arXiv Machine Learning →

The paper identifies that low‑timestep deep spiking Q‑networks (DSQNs) suffer mainly from common‑mode errors that degrade Q‑value estimation. To address this, the authors introduce Common‑Mode Compensation DSQN (CMC‑DSQN), which employs an auxiliary ANN during training to correct these errors while keeping inference energy‑efficient. Experiments on Atari and MiniAtar show that CMC‑DSQN improves performance by nearly 20 % at two timesteps and outperforms both DSQN baselines and the ANN baseline at four timesteps.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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