A Case Study in Hybrid Multi-threading and Hierarchical Reinforcement Learning Approach for Cooperative Multi-agent Systems

2015 ◽  
Author(s):  
Hiram Ponce ◽  
Ricardo Padilla ◽  
Alan Davalos ◽  
Alvaro Herrasti ◽  
Cynthia Pichardo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Hierarchical Reinforcement Learning ◽  
Multi Agent

scholarly journals SYSTEM ANALYSIS IN HIERARCHICAL INTELLIGENT MULTI-AGENT SYSTEMS

2021 ◽  
pp. 33-46
Author(s):  
V. S. Simankov ◽  
Yu. V. Dubenko
Keyword(s):  
Reinforcement Learning ◽  
System Analysis ◽  
Three Dimensional ◽  
Label Propagation ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Hierarchical Reinforcement Learning ◽  
Multi Agent ◽  
Weight Coefficients

The system analysis of the hierarchical intelligent multi-agent system in general, as well as its main structural unit, the intelligent agent, its major subsystems identified. As part of the analysis of the computer vision subsystem, it was concluded that the considered sources have insufficiently worked out issues related to the processing of occlusions, with the automation of the process of reconstruction of three-dimensional scenes, with the implementation of the processing of an unstructured set of images. The structure of the block for the reconstruction of three-dimensional scenes is proposed, the implementation of which is aimed at eliminating the indicated problems characteristic of the machine vision subsystem. The analysis of the main methods of implementing unsupervised learning is carried out, based on the results of which it is concluded that it is advisable to use reinforcement learning when implementing systems of this type. Such types of reinforcement learning as hierarchical reinforcement learning and multi-agent reinforcement learning are considered. A method for segmentation of macro actions is proposed, based on the implementation of clustering by the method of label propagation, in which the number of transitions is formalized in the form of weight coefficients of edges.

scholarly journals A Programming Approach to Collective Autonomy

2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Roberto Casadei ◽  
Gianluca Aguzzi ◽  
Mirko Viroli
Keyword(s):  
Autonomous Vehicles ◽  
Autonomic Computing ◽  
Autonomous Agents ◽  
Programming Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Simulated Case ◽  
Complex Settings

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

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