  Reconfigurable intelligent surfaces (RISs) are a promising technology to
enable smart radio environments. However, integrating RISs into wireless
networks also leads to substantial complexity for network management. This work
investigates heuristic algorithms and applications to optimize RIS-aided
wireless networks, including greedy algorithms, meta-heuristic algorithms, and
matching theory. Moreover, we combine heuristic algorithms with machine
learning (ML), and propose three heuristic-aided ML algorithms, namely
heuristic deep reinforcement learning (DRL), heuristic-aided supervised
learning, and heuristic hierarchical learning. Finally, a case study shows that
heuristic DRL can achieve higher data rates and faster convergence than
conventional deep Q-networks (DQN). This work provides a new perspective for
optimizing RIS-aided wireless networks by taking advantage of heuristic
algorithms and ML.
