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CIA
2007
Springer

Multi-agent Learning Dynamics: A Survey

9 years 5 months ago
Multi-agent Learning Dynamics: A Survey
Abstract. In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration and policy iteration. Four characteristics are studied: initial conditions, parameter settings, convergence speed, and local versus global convergence. Global convergence is still difficult to achieve in practice, despite existing theoretical guarantees. Multiple visualizations are included to provide a comprehensive insight into the learning dynamics.
H. Jaap van den Herik, Daniel Hennes, Michael Kais
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CIA
Authors H. Jaap van den Herik, Daniel Hennes, Michael Kaisers, Karl Tuyls, Katja Verbeeck
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