Pankayaraj Pathmanathan

University of Peradeniya | UOP · Department of Computer Engineering

In recent years, extensive research has been carried out in the field of autonomous aerial vehicle control, motivated by the rapid advancements in Machine Learning (ML). In particular , Reinforcement Learning (RL) has gained immense interest in developing control algorithms. In this work, we examine key control problems related to the operation of...

In multi-agent systems, an agent's behaviour is affected by the dynamicity of the environment and that of the interactions among agents. Thus, in learning to cooperate to solve complex tasks, such considerations should be taken into account. Reinforcement learning has gained immense interest in this line of research as it allows agents to learn use...

This paper considers a multi-armed bandit (MAB) problem in which multiple mobile agents receive rewards by sampling from a collection of spatially dispersed stochastic processes, called bandits. The goal is to formulate a decentralized policy for each agent, in order to maximize the total cumulative reward over all agents, subject to option availab...

This paper proposes a novel policy for a group of agents to, individually as well as collectively, solve a multi armed bandit (MAB) problem. The policy relies solely on the information that an agent has obtained through sampling of the options on its own and through communication with neighbors. The option selection policy is based on an Upper Conf...

Sleep apnea is a breathing disorder where a person repeatedly stops breathing in sleep. Early detection is crucial for infants because it might bring long term adversities. The existing accurate detection mechanism (pulse oximetry) is a skin contact measurement. The existing non-contact mechanisms (acoustics, video processing) are not accurate enou...

Existing studies of the Multi Agent Multi Armed Bandit (MAMAB) problem, with the exception of a very few, consider the case where the agents observe their neighbors according to a static network graph. They also mostly rely on a running consensus for the estimation of the option rewards. Two of the exceptions consider a problem where agents observe...