Ricardo Bagnasco

From SimpleWiki
Revision as of 19:51, 24 November 2009 by Simpleweb (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigationJump to search
Student: Ricardo Bagnasco
Title: Autonomous Network Management in Multi Objective

Environments by means of Reinforced Learning Agents

e-mail: rbagnasco@tsc.upc.edu
Affiliation: UPC
Supervisor: Joan Serrat
Start: 2007
End: 2011
Funding: Ministerio de Innovación y Ciencia (FPI)


PhD project description

This research is intended to contribute to the autonomous management of networks, allowing policies to be dynamically adjusted and aligned to application directives according to the available resources. Many existing management approaches requires static a priori policy deployment but our proposal goes one step further modifying initially deployed policies by learning from the system behaviour. We use a hierarchical policy model to show the connection of high level goals with network level configurations. We also intend to solve two important and mostly forgotten issues: the system has multiple goals some of them contradictory and we will show how to overcome it; and, some current works optimize one network element but being unaware of others participants; instead, our proposed scheme takes into account various social behaviours, such as cooperation and competition among different elements.


  1. R. Bagnasco, J. Serrat, “MARL (multi agent reinforcement learning) in P2P Network Management”. 2nd EMANICS Workshop on Peer-to-Peer Management, London, 27-28 April 2009
  2. R. Bagnasco, J. Serrat. “Learning and adapting policies in Computer Networks”. ICAC 2009 Doctoral Consortium. 15-19 June 2009 (submitted)
  3. R. Bagnasco y J. Serrat. “Multi-Agent Reinforcement Learning in Network Management”, 3th Aims Conference. University of Twente, Netherlands. 30 June, 2009. (submitted)

Additional information

External links

  • [_URL_ Homepage] of Ricardo Bagnasco
  • Publications of Ricardo Bagnasco, as indexed by DBLP