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View article: Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes Open
Partially observable Markov decision processes (POMDPs) are a general mathematical model for sequential decision-making in stochastic environments under state uncertainty. POMDPs are often solved online, which enables the algorithm to adap…
View article: Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction Open
Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challenge in the traditional offline RL setting, where an agent m…
View article: NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes
NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes Open
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 environments pose significant challenges to traditional de…
View article: Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue Open
Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We present a comprehensive approach to optimi…
View article: Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes Open
A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Such problems are traditionally modeled as non-stationary Mark…
View article: Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems
Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems Open
Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of th…