A robust approach to robot team learning

Girard, Justin; Emami, M. Reza

doi:10.1007/s10514-015-9524-2

Cited by 1 publication

(2 citation statements)

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“…As in [6], a particle filter is implemented to estimate each of the state variable for every robot. Each simulation from experiment 2 is performed again, but this time with the particle filter utilized.…”

Section: Experiments 3: Preference Advice With Team Learning Measuremmentioning

confidence: 99%

“…The particle filter estimates position and orientation information about the robot's environment that is necessary to form the individual learning state s I . The formulation for the particle filter can be found in [6]. Filtering the robot's state information enables the individual and team learning algorithms to maintain their performance in the presence of noisy sensory data.…”

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confidence: 99%

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An Advice Mechanism for Heterogeneous Robot Teams

Daniluk¹,

Emami²

2020

International Journal of Robotics and Automation

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The use of reinforcement learning for robot teams has enabled complex tasks to be performed, but at the cost of requiring a large amount of exploration. Exchanging information between robots in the form of advice is one method to accelerate performance improvements. This thesis presents an advice mechanism for robot teams that utilizes advice from heterogeneous advisers via a method guaranteeing convergence to an optimal policy. The presented mechanism has the capability to use multiple advisers at each time step, and decide when advice should be requested and accepted, such that the use of advice decreases over time. Additionally, collective collaborative, and cooperative behavioural algorithms are integrated into a robot team architecture, to create a new framework that provides fault tolerance and modularity for robot teams.ii

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Section: Experiments 3: Preference Advice With Team Learning Measuremmentioning

confidence: 99%

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confidence: 99%