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Found 9 papers, 0 papers with code
Multi-Agent Resilient Consensus under Intermittent Faulty and Malicious Transmissions (Extended Version)
In this work, we consider the consensus problem in which legitimate agents share their values over an undirected communication network in the presence of malicious or faulty agents.
Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)
We provide two main theoretical results: 1) characterize the number of taxis $m$ that is sufficient for IA to be stable; 2) derive a necessary condition on $m$ to maintain stability for IA as time goes to infinity.
Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare
For our experimental results we consider real NYC ride share data for the year 2022 and information for more than 2000 events across 300 unique venues in Manhattan.
Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand
We propose a mechanism for switching the originally trained offline approximation when the current demand is outside the original validity region.
Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks
The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center.
Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems
Further, the expected convergence rate decays exponentially with the quality of the trust observations between agents.
Optimization and Control Robotics Systems and Control Signal Processing Systems and Control
Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems
Our methods specifically address the computational challenges of partially observable multiagent problems.
Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks
We refer to this hybrid communication scheme as a cloud-cluster architecture.
Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems
In this paper we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, and partial state observations.
