  Lagrangian-guided Monte Carlo tree search with global dual ascent has been
applied to solve large constrained partially observable Markov decision
processes (CPOMDPs) online. In this work, we demonstrate that these global dual
parameters can lead to myopic action selection during exploration, ultimately
leading to suboptimal decision making. To address this, we introduce
history-dependent dual variables that guide local action selection and are
optimized with recursive dual ascent. We empirically compare the performance of
our approach on a motivating toy example and two large CPOMDPs, demonstrating
improved exploration, and ultimately, safer outcomes.
