Bayesian POMDP

How it works

Comparisons

Evidence quality

Highlighted claims

• The Bayesian POMDP agent maintains a posterior distribution over source locations, updated by Bayes' rule after each observation using empirically estimated detection likelihoods. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
• The Bayesian agent produces broad recovery trajectory distributions in which all five geometric metrics vary widely with prior detection history, even when the triggering condition is identical. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
• The performance gap between the clock-state agent and the Bayesian agent is real but modest, and grows most in sparse environments where history-dependent adaptation confers the largest marginal advantage. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
• The Bayesian POMDP baseline neglects temporal correlations between successive odor observations, which are present in real turbulent flow; incorporating them would improve accuracy but substantially increase computational cost. — Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery