3 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +1
Partially observable Markov decision processes (POMDPs) are a general mathematical model for sequential decision-making in stochastic environments under state uncertainty. POMDPs a…
NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes
Nathaniel S. Keplinger, Baiting Luo, Iliyas Bektas +5
In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary…
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
Baiting Luo, Yunuo Zhang, Abhishek Dubey +1
A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Suc…
Decision Making in Non-Stationary Environments with Policy-Augmented Search
Ava Pettet, Yunuo Zhang, Baiting Luo +5
Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (…