LP-BTS is a learning-guided planning architecture for large dynamic action spaces, specifically designed for one-to-many mobile charging scenarios. It uses a graph proposal policy, a learned value critic, and edge-budgeted PUCT to evaluate and select actions. The architecture achieves state-of-the-art results in a controlled setting, outperforming various baselines and providing evidence for the effectiveness of learning-guided planning in complex action spaces.