5 citations · 9 across the 5 of their papers we have counts for
7 papers
Conformal Temporal Logic Planning using Large Language Models
Jun Wang, Jiaming Tong, Kaiyuan Tan +2
This paper addresses planning problems for mobile robots. We consider missions that require accomplishing multiple high-level sub-tasks, expressed in natural language (NL), in a te…
Targeted Adversarial Attacks against Neural Network Trajectory Predictors
Kaiyuan Tan, Jun Wang, Yiannis Kantaros
Trajectory prediction is an integral component of modern autonomous systems as it allows for envisioning future intentions of nearby moving agents. Due to the lack of other agents'…
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan +5
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches ove…
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
Xian Yeow Lee, Yasaman Esfandiari, Kai Liang Tan +1
Advances in computing resources have resulted in the increasing complexity of cyber-physical systems (CPS). As the complexity of CPS evolved, the focus has shifted from traditional…
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
Kai Liang Tan, Yasaman Esfandiari, Xian Yeow Lee +2
Adoption of machine learning (ML)-enabled cyber-physical systems (CPS) are becoming prevalent in various sectors of modern society such as transportation, industrial, and power gri…
Deep Reinforcement Learning for Adaptive Traffic Signal Control
Kai Liang Tan, Subhadipto Poddar, Anuj Sharma +1
Many existing traffic signal controllers are either simple adaptive controllers based on sensors placed around traffic intersections, or optimized by traffic engineers on a fixed s…
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan +2
Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of incr…