Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging
LP-BTS uses graph proposal policies, learned critics, and budgeted PUCT search to plan mobile charging across dynamic action spaces up to 2,813 stops.
LP-BTS is a learning-guided planning architecture for one-to-many mobile charging, where N=250 sensors induce roughly 1,125 initial candidate charging stops. A graph proposal policy concentrates candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares simulated futures, letting a single frozen checkpoint cover action universes from 736 to 2,813 stops. On a sealed 30-scenario confirmatory bank it attains the highest observed survival (0.4545) and alive-AUC (0.8031), though its +0.0066 survival edge over the strongest engineered comparator is statistically unresolved.
- Handles dynamic action universes from 736 to 2,813 charging stops with one frozen checkpoint.
- Uniform sampling costs 8.8 survival percentage points in matched ablations.
- Sealed 30-scenario bank: highest survival (0.4545) and alive-AUC (0.8031).
- Advantage over strongest domain-engineered comparator (+0.0066) is statistically unresolved.
From the original article
1 min read at arxiv.orgMany learned sequential decision systems map the current state directly to an action. That shortcut becomes brittle when candidate actions are numerous, geometrically structured, and rebuilt with the state.
By Liang-Ching Tao · arxiv.org
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