Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration

arXiv:2603.03595v2 Announce Type: replace Abstract: Coordinating multiple autonomous agents to explore and serve spatially heterogeneous demand requires jointly learning unknown spatial patterns and planning trajectories that maximize task performance. Pure model-based approaches provide structured uncertainty estimates but lack adaptive policy learning, while deep reinforcement learning often suffers from poor sample efficiency when spatial priors are absent. This paper presents a hybrid belief-reinforcement learning (HBRL) framework to address this gap. In the first phase, agents construct spatial beliefs using a Log-Gaussian Cox Process (LGCP) and execute information-driven trajectories guided by a Pathwise Mutual Information (PathMI) planner with multi-step lookahead. In the second phase, trajectory control is transferred to a Soft Actor-Critic (SAC) agent, warm-started through dual-channel knowledge transfer: i) belief state transfer supplies spatial uncertainty, and ii) replay buffer seeding provides demonstration trajectories generated during LGCP exploration. A variance-normalized overlap penalty enables coordinated coverage through shared belief state, permitting cooperative sensing in high-uncertainty regions while discouraging redundant coverage in well-explored areas. The framework is instantiated in two scenarios: multi-Unmanned Aerial Vehicle (UAV) wireless service provisioning, and mobility-on-demand sensing over measured New York City taxi demand. In the wireless scenario it achieves 10.8% higher cumulative reward and takes 38% fewer episodes to reach the reward level of the baselines, with ablation studies confirming that dual-channel transfer outperforms either channel alone; the taxi scenario yields consistent gains with all algorithmic components unchanged.