22 citations · 22 across the 1 of their papers we have counts for
3 papers
eess.SP2021★ 22 cited
Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning
Qisheng Wang, Xiao Li, Shi Jin +1
In this letter, we investigate the hybrid beamforming based on deep reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU) multiple-input-single-output (MISO) sy…
eess.SP2019
PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning
Qisheng Wang, Keming Feng, Xiao Li +1
In this letter, we investigate the hybrid beamforming for millimeter wave massive multiple-input multiple-output (MIMO) system based on deep reinforcement learning (DRL). Imperfect…
cs.LG2019
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration
Qisheng Wang, Qichao Wang
Exploration efficiency is a challenging problem in multi-agent reinforcement learning (MARL), as the policy learned by confederate MARL depends on the collaborative approach among…