Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging

arXiv:2609.17429 · cs.LG, cs.AI · Submitted 2026-09-15 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-15

Updated: 2026-09-15

Comments: 15 pages, 7 figures. Learning-guided planning, budgeted tree search, PUCT, and sequential decision-making in large dynamic action spaces

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Ablation — 14× in this paper · explained in 1 episode
• Action Space — 12× in this paper · explained in 1 episode
• AUC — 7× in this paper · explained in 2 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Q-learning — 3× in this paper · explained in 3 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

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