A reinforcement learning framework formulated as an event-driven semi-Markov decision process with graph states and action masking outperforms heuristic and optimization baselines for stochastic electric truck routing under charging constraints.
Ibe, Markov Processes for Stochastic Modeling, Elsevier
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Learning to Route Electric Trucks Under Operational Uncertainty
A reinforcement learning framework formulated as an event-driven semi-Markov decision process with graph states and action masking outperforms heuristic and optimization baselines for stochastic electric truck routing under charging constraints.