A maximum entropy reinforcement learning framework generates realistic customer trajectories in retail spaces that match real data better than TSP or PNN heuristics and support more accurate layout optimization decisions.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights
A maximum entropy reinforcement learning framework generates realistic customer trajectories in retail spaces that match real data better than TSP or PNN heuristics and support more accurate layout optimization decisions.