A constrained multi-agent RL tracker with a linear-assignment safety layer and cost-margin gradients improves particle track reconstruction on simulated proton-CT data.
Learning to drive in a day,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.comp-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning
A constrained multi-agent RL tracker with a linear-assignment safety layer and cost-margin gradients improves particle track reconstruction on simulated proton-CT data.