3 papers
cs.LG2021
Interpretable Local Tree Surrogate Policies
John Mern, Sidhart Krishnan, Anil Yildiz +2
High-dimensional policies, such as those represented by neural networks, cannot be reasonably interpreted by humans. This lack of interpretability reduces the trust users have in p…
cs.LG2020
Improved POMDP Tree Search Planning with Prioritized Action Branching
John Mern, Anil Yildiz, Larry Bush +2
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. This paper proposes a method called PA-POMCPOW to sa…
cs.AI2020
Bayesian Optimized Monte Carlo Planning
John Mern, Anil Yildiz, Zachary Sunberg +2
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening at…