Tag
This paper proposes complex-valued unitary recurrent networks (uRNNs) as drop-in replacements for recurrent PPO architectures, using phase-preserving dynamics inspired by quantum state measurement to address partial observability. The authors report up to 3x the reward of baselines on memory-improvable tasks such as rocksample and Craftax, with code released publicly.
Proposes CRFCAN, a complex-valued residual network for joint channel and phase noise estimation in sub-THz OFDM systems, which achieves end-to-end recovery with physics-inspired structure and outperforms conventional and state-of-the-art deep learning models.