Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration

• URL: http://arxiv.org/abs/2603.03595v1
• Date: Wed, 04 Mar 2026 00:00:34 GMT
• Title: Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration
• Authors: Danish Rizvi, David Boyle,
• Abstract summary: Pure model-based approaches provide structured uncertainty estimates but lack adaptive policy learning.<n>This paper presents a hybrid belief-reinforcement learning framework to address this gap.<n>Results show 10.8% higher cumulative reward and 38% faster convergence over baselines.
• Score: 3.0222726254970174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 through shared belief state, permitting cooperative sensing in high-uncertainty regions while discouraging redundant coverage in well-explored areas. The framework is evaluated on a multi-UAV wireless service provisioning task. Results show 10.8% higher cumulative reward and 38% faster convergence over baselines, with ablation studies confirming that dual-channel transfer outperforms either channel alone.

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