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Found 10 papers, 7 papers with code
Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
Since directly sampling from the ideal state distribution given the latest observation and previous state is infeasible, particle filters approximate the posterior belief distribution by propagating states and adjusting weights through prediction and resampling steps.
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Notably, across tasks ranging from continuous control with inherently stochastic dynamics to high-dimensional robotic hand manipulation, L-MAP significantly outperforms existing model-based methods and performs on-par with strong model-free actor-critic baselines, highlighting the effectiveness of the proposed approach in planning in complex and stochastic environments with high-dimensional action spaces.
NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes
This toolkit is the first effort to develop a set of standardized interfaces and benchmark problems to enable consistent and reproducible evaluation of algorithms under non-stationary conditions.
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
We present a comprehensive approach to optimize UAV-based search and rescue operations in neighborhood areas, utilizing both a 3D AirSim-ROS2 simulator and a 2D simulator.
Decision Making in Non-Stationary Environments with Policy-Augmented Search
In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
However, existing approaches for decision-making in NSMDPs have two major shortcomings: first, they assume that the updated environmental dynamics at the current time are known (although future dynamics can change); and second, planning is largely pessimistic, i. e., the agent acts ``safely'' to account for the non-stationary evolution of the environment.
Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems
To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching.
ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA
A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system.
Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems
Our samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83% and 92%, compared to 56%, 66% and 71% of the grid, random and Halton samplers, respectively.
Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework
While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating.
Cryptography and Security
