Modelling the dynamics of multiagent Q-learning with ε-greedy exploration

ABSTRACT

We present a framework to model the dynamics of Multiagent Q-learning with ε-greedy exploration. The applicability of the framework is tested through experiments in typical games selected from the literature.

REFERENCES

• 1: Karl Tuyls , Katja Verbeeck , Tom Lenaerts, A selection-mutation model for q-learning in multi-agent systems, Proceedings of the second international joint conference on Autonomous agents and multiagent systems, July 14-18, 2003, Melbourne, Australia [doi>10.1145/860575.860687]
• 2: Richard S. Sutton , Andrew G. Barto, Introduction to Reinforcement Learning, MIT Press, Cambridge, MA, 1998
• 3: José M. Vidal , Edmund H. Durfee, Predicting the Expected Behavior of Agents that Learn About Agents: The CLRI Framework, Autonomous Agents and Multi-Agent Systems, v.6 n.1, p.77-107, January 2003 [doi>10.1023/A:1021765422660]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

Keywords:
ε-greedy, Q-learning, multiagent learning, reinforcement learning