[2603.03595] Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration

Computer Science > Machine Learning arXiv:2603.03595 (cs) [Submitted on 4 Mar 2026] Title:Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration Authors:Danish Rizvi, David Boyle View a PDF of the paper titled Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration, by Danish Rizvi and 1 other authors View PDF HTML (experimental) 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: belief state initialization supplies spatial uncertainty, and replay buffer seeding provides demonstration trajectories generated during LGCP exploration. A variance-normalized overlap penalty enables coordinated coverage thr...