11 citations · 34 across the 10 of their papers we have counts for
10 papers
Robust Multi-Agent Reinforcement Learning with State Uncertainty
Sihong He, Songyang Han, Sanbao Su +3
In real-world multi-agent reinforcement learning (MARL) applications, agents may not have perfect state information (e.g., due to inaccurate measurement or malicious attacks), whic…
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications
Jiangwei Wang, Shuo Yang, Ziyan An +5
Reward design is a key component of deep reinforcement learning, yet some tasks and designer's objectives may be unnatural to define as a scalar cost function. Among the various te…
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation
Sanbao Su, Songyang Han, Yiming Li +4
Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboar…
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
Songyang Han, Shanglin Zhou, Lynn Pepin +3
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and ca…
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Songyang Han, Sanbao Su, Sihong He +4
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information. However, polici…
Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios
Zhili Zhang, Songyang Han, Jiangwei Wang +1
Communication technologies enable coordination among connected and autonomous vehicles (CAVs). However, it remains unclear how to utilize shared information to improve the safety a…
Uncertainty Quantification of Collaborative Detection for Self-Driving
Sanbao Su, Yiming Li, Sihong He +4
Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still…
Botnets Breaking Transformers: Localization of Power Botnet Attacks Against the Distribution Grid
Lynn Pepin, Lizhi Wang, Jiangwei Wang +5
Traditional botnet attacks leverage large and distributed numbers of compromised internet-connected devices to target and overwhelm other devices with internet packets. With increa…
Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles
Songyang Han, He Wang, Sanbao Su +2
With the development of sensing and communication technologies in networked cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based methodologies are integra…
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles
Songyang Han, Shanglin Zhou, Jiangwei Wang +4
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) communication.…