Dynamic role assignment using general value functions

Saminda Abeyruwan, Andreas Seekircher, Ubbo E Visser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Collecting and maintaining accurate world knowledge in a dynamic, complex, competitive, and stochastic environment such as RoboCup 3D Soccer Simulation is a challenging task. Knowledge should be learned in real-time with time constraints. We use recently introduced Off-Policy Gradient Descent algorithms in Reinforcement Learning that illustrate learnable knowledge representations for dynamic role assignments. The results show that the agents have learned competitive policies against the top teams from RoboCup 2012 for three vs three, five vs five, and seven vs seven agents. We have explicitly used subset of agents to identify the dynamics and the semantics for which the agents learn to maximize their performance measures, and to gather knowledge about different objectives so that all agents participate effectively within the team.

Original languageEnglish (US)
Title of host publicationAAMAS 2013 Workshop on Adaptive and Learning Agents, ALA 2013
ISBN (Print)9781943580125
StatePublished - 2013
EventAAMAS 2013 Workshop on Adaptive and Learning Agents, ALA 2013 - Saint Paul, United States
Duration: May 6 2013May 7 2013


OtherAAMAS 2013 Workshop on Adaptive and Learning Agents, ALA 2013
Country/TerritoryUnited States
CitySaint Paul


  • Dynamic role assignment function
  • GQ(λ)
  • Greedy-GQ(λ)
  • Off-Policy Prediction and Control
  • Reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software


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