GNN enhanced reinforcement learning for robot navigation in complex topological networks

To address the challenges encountered by intelligent robots in perceiving high-dimensional environmental states and making adaptive trajectory planning decisions in complex topological environments, this paper presents a Graph Neural Network–Reinforcement Learning (GNN-RL) integrated framework, implemented based on the Soft Actor-Critic (SAC) algorithm for continuous control tasks. First, leveraging the topological modeling capability of GNNs, environmental entities are abstracted into graph nodes, and their spatial constraints and semantic associations are encoded as edge features. Through multi-layer graph convolution and adaptive edge weighting, high-dimensional structured environmental information is compressed into low-dimensional node-level and graph-level embeddings with rich topological semantics. This provides structured environmental cognition for the subsequent reinforcement learning module, alleviating the curse of dimensionality and enabling efficient action selection. Second, a dynamic collaborative mechanism between the GNN encoder and the SAC-based RL agent is established. The topological features extracted by the GNN are fed as input to the RL agent, which consists of twin Q-networks, a policy network, and a value network. A multi-objective reward function, which integrates safety, progress, and motion smoothness, guides the agent’s trial-and-error exploration. In this manner, static topological representations are transformed into dynamic trajectory policies, while the GNN parameters are jointly optimized end-to-end via the gradient signals from the RL loss function, overcoming the limitations of purely static graph learning. Finally, comprehensive comparative experiments are conducted in simulated complex topological environments, evaluating the proposed GNN-RL approach against DQN, PPO, and A* algorithms. The results show that the GNN-RL method achieves a favorable balance between perception accuracy and decision-making efficiency, providing a reliable and adaptive solution for robot navigation and trajectory planning in structured, dynamic environments.