1 code implementation • 6 Jan 2024 • Ava Pettet, Yunuo Zhang, Baiting Luo, Kyle Wray, Hendrik Baier, Aron Laszka, Abhishek Dubey, Ayan Mukhopadhyay
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we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.
1 code implementation • 3 Jan 2024 • Baiting Luo, Yunuo Zhang, Abhishek Dubey, Ayan Mukhopadhyay
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.
no code implementations • 20 Feb 2023 • Baiting Luo, Shreyas Ramakrishna, Ava Pettet, Christopher Kuhn, Gabor Karsai, Ayan Mukhopadhyay
To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching.
1 code implementation • 19 Jul 2022 • Shreyas Ramakrishna, Baiting Luo, Christopher Kuhn, Gabor Karsai, Abhishek Dubey
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.
1 code implementation • 28 Feb 2022 • Shreyas Ramakrishna, Baiting Luo, Yogesh Barve, Gabor Karsai, Abhishek Dubey
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.
no code implementations • 15 Feb 2021 • Xiangguo Liu, Baiting Luo, Ahmed Abdo, Nael Abu-Ghazaleh, Qi Zhu
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