4 papers · 1 filter
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +2
In Partially Observable Markov Decision Processes (POMDPs), maintaining and updating belief distributions over possible underlying states provides a principled way to summarize act…
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…
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Baiting Luo, Ava Pettet, Aron Laszka +2
Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challe…
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…